I posted 147 times in X communities in the past few weeks using an AI content repurposing system I built.

First thing I’ll say: this campaign was mediocre as far as results go.

I don’t know if it’s something I’ll continue. I’m certainly looking for better options.

But it’s ok for certain situations. It’s not hard to run and gets some results.

So despite it being a failed experiment in my terms, some of you may still want to know about it.

The results:

How I built this system

It’s a lot like my Reddit system, which I detailed out here:

Though I have made some particular improvements on the content side.

First thing I did was go through all of the X communities that I wanted to market myself in. I took a wide breath here:

• Solopreneur community

• Startup communities

• Building public communities

• Indie SaaS communities

• B2B SaaS lead gen

I had seven communities in total.

Then I went through each community and collected content that did above average. If the average post got two or three likes, I was looking for stuff that got 20-50 plus likes and I saved that into a swipe file.

As you’d see in my Reddit campaign post, the system takes my source content and repurposes it to write social media posts based on what’s in that swipe file.

It also aligns the new post with the target audience on the community. It uses a very basic target audience snippet that only has pain points and dream outcomes.

AKA the content creation prompt has 3 variables:

1. The source content to base the new content on

2. The example post that performed well on the channel

3. A target audience snippet with pain points and dream outcomes.

The prompt runs automatically for every example post in my swipe file when I drop a new piece of source content in.

With every new piece of source content, I get around 80 X posts.

The editing challenge

I ran 4 new pieces of source content, which left me with 300+ posts to revise and schedule.

And yes, revising is critical. These posts are not good enough to publish.

I took the time to build out a few scheduling automations to get rid of that problem (this saved me a ton of time, but was complicated to build).

With an hour of editing, I can get out about 20-25 posts.

Not bad at all, but you do have to be pretty consistent about it.

The results

The average post is something like 30 impressions. So not very good as far as posts go.

But like I said I can do 20 per hour. So I end up with 600 impressions per hour of editing work.

That’s not horrible.

Some days I got 10-20 impressions on my website attributed to X. Which really isn’t anything to to write about (even though I am).

As I said in the beginning, these are not results that would typically encourage me to keep going. But there are some benefits.

The silver lining

The core advantage is the new followers.

The campaign does a pretty good job of putting me in front of the right people.

For example, most of my impressions are from the US.

And most of my impressions are from new people. Which would not be the case if I were just posting in my feed.

Given those 2 data points, this is more beneficial in my eyes than:

1. Being a reply guy (lots of foreign impressions… which is not useful for my business)

2. Posting on my feed (which wouldn’t get me in front of enough new people)

How I’d optimize this

If I decide this campaign is worth optimizing, here is how I would do it.

Fortunately, I collect the data on post performance based on the prompt that created it.

First, I would go ahead and delete the bottom 20%. Or keep going as is until I have more significant data per prompt and get rid of the bottom half.

That would look like deleting 40 prompts. I’d do some more research and add in 20 new ones.

Every month or so I’d do this process again. Delete the bottom performers, add a few new ones, hopefully getting from 40 impressions per post to 100.

That alone would bring me to 2,000 impressions per hour and is achievable.

Next, I would complain very loudly about postpone.app not having a built it feature to toggle the “also share with followers” feature that X gives you when you post natively.

I know they could easily add this. And it would immediately bump 30-40% campaign performance.

If they don’t comply, I would take the time to figure out a solution for this.

With those two optimizations this could get to 2,600 impressions per hour of work.

To me, that’s good enough. And here’s the thing.

If that toggle is checked, and every post is also being posted to my feed, that creates a flywheel where I’m getting both the benefit of community views and the compounding views of my growing following.

It would dramatically change the long term ROI of this campaign.

Alright that’s all I got for you today!

You see that image right above this? It was, very obviously, created by AI.

Maybe you noticed every one of my articles has a similar image with the same animated characters. So do my X, LinkedIn, and Reddit banner photos.

Each of those takes me about a minute to create. I don’t even write the prompt to create them.

They’re not perfect, but if I had to hire an illustrator I’d be looking at a few hundred dollars each. I certainly could not have done it myself, regardless of how much effort I was willing to spend.

It’s a good example of using AI to do what I would have never thought cost effective before.

Why do it? Well… partly because it’s fun. But really because now every time my readers come to my newsletter, there’s a character they will become familiar with over time.

The animation of myself, and that little robot, I think we’ll call him Erni, will come to represent the ideas I write about.

Here I’ll show you how I set this up and how you can too.

My visionary genius

Before, the imagery on my newsletter was inexistent and thus un-inviting. I was bored with my lack of visuals.

It’d be nice to think all that matters is the content: my walkthroughs, case studies, strategies. But the reality is visuals draw people in more than words do.

It needs to represent my brand, and be consistent. The consistency is really the main challenge with using AI.

For every post I have a scene that’s being played out in an animated image.

The scene always has an animated avatar of me and Erni the robot (duplicated as many times as needed).

Given this newsletter’s about ways to have AI do more of your marketing, Erni pretty much represents that. So in each scene you see me doing nothing, and Erni doing the work.

And as you can see, it’s consistent!

It’s not perfect. Often it wants to change small details of Erni. But everything is close enough to create brand recognition.

How I did this

I use my Notion agent to help me with all my work. Here’s an article on how I set that up:

The first thing I did was write my concept, what I needed, the system I needed for producing these consistent graphics and what the media was going to be for, into a task.

I included some examples of other creators doing good AI imagery. If they had articles explaining how they did it, I included that too.

Then, all I did was prompt the notion agent to do some research and figure out how to pull this off for me.

It showed me stuff I was expecting: MidJourney, ChatGPT, etc..

But it also found something I hadn’t seen before. Whisk.

A few minutes of investigating later, I knew whisk what the tool for the job.

How Whisk works

Whisk is a tool from Google’s labs.google.

The tool breaks the image you’re trying to create into 3 variables:

1. Subject/s - The characters in the photo.

2. Scene - The place the photo takes place.

3. Style - The style you want the image in.

For each variable, you can either prompt what you want, and it generates the asset for you, or you can upload the image directly.

It uses Nano-Banana to accurately place your assets into the photo.

The trick: everything needs to be in the right style

The first stage of working with any AI tool is frustration.

After more error than trial, I learned it’s really not enough to put your different assets in and expect consistency. There seems to be a lost in translation effect with the word consistency itself.

Here is an early example. The scene is the pixar/illumination style animation that I asked for in the style.

But image of me looks more photorealistic. It’s consistent with the image I gave it of me, but not with the scene. (Also in real life I’m much better looking).

I quickly realized that each variable I gave it had to already be in the correct style.

Because the subjects are the same every time, I only had to create them once. I moved over to ChatGPT for this.

I uploaded the profile picture I use for social media, and told it to create a version of this in illumination’s animation style.

Pretty good result.

Next was the robot. I didn’t have a particular robot in mind, so this took more prompting. But after some back and forth I ended up with this character:

As you can see, it’s already in the illumination animation style.

The scene is different every time however. I got the style consistent by including my style prompt with the scene prompt.

This is the style prompt I give it every time:

Illumination Studios animation style, 3D CGI, soft rounded character design, oversized expressive eyes, simplified toony anatomy, chunky proportions, soft diffuse lighting, warm color palette, glossy eye highlights, smooth surfaces, vibrant saturated colors, Pixar-quality render, family-friendly appeal

This goes in the style variable in Whisk. BUT to make sure everything is consistent, it is also in the bottom of the scene prompt. Like this:

Modern digital workspace environment with floating Notion interface elements, organized document pages arranged in systematic layers, clean white background with subtle Matrix Green (#20C964) accent lighting highlighting connection points between pages, 

Illumination Studios animation style, 3D CGI environment, soft rounded architecture, simplified geometry, warm diffuse lighting, vibrant harmonious color palette, gentle depth of field, soft atmospheric haze, clean organized composition, slightly exaggerated proportions, golden hour lighting, family-friendly inviting atmosphere, Pixar-quality render, no harsh shadows

Here’s the image these variables created.

Making it repeatable

The last prompt you need to be aware of is the main prompt. This is the core query that puts all the assets together and generates the final image.

The scenes are unique every time. And they need to be pretty intricate. It needs to include:

• Character placement and positioning in frame

• What each character is doing (specific actions)

• Character interactions and relationships

• Arm movements and gestures

• Direction characters are facing from camera

• Background character details (if any)

• Foreground/midground/background elements

• Lighting direction and mood

• Camera angle and framing

If you’re anything like me, you may be feeling a bit lazy at this point, and don’t want to do all this prompting.

Relax. I’ve done all the avoiding work for you.

In fact, once I got the system down, I stopped writing all the prompts altogether.

Every time I finish an article, I tell my Notion agent to generate all the Whisk prompts for the article.

To understand how I did that you need to:

1. Read this article on how I set up my Notion AI - The same article I mentioned earlier.

2. Know that I made a playbook/SOP on how I want these prompts written.

That playbook includes the default style prompt, how to come up with the setting and write the prompt, and how to put everything together into the main prompt.

If you want that playbook, email me and I’ll send it over.

If you’re shy, the article on setting up my Notion AI includes how I create these playbooks.

The final workflow

After having all this together, here is the final workflow that takes about 3 minutes and results in all my newsletter hero images.

1. Finish article in Notion

2. Prompt Notion AI “Create whisk prompts for this article using AI Brand Asset Generation Playbook”

3. Open Whisk.

4. Upload ME avatar and robot images

5. Paste setting prompt from Notion AI

6. Paste style prompt from Notion AI

7. Paste main prompt from Notion AI

8. Hate first image. Be picky for no reason.

9. Change main prompt slightly, or ask Notion AI to go a different direction.

10. Like output. Download.

11. Upload to substack.

Frankly, it took me longer to write this article for you than it did to do ALL of this.

Have fun.

Obviously, we’re all looking for ways to be more productive with AI.

I thought I had it down.

I built a few AI automations that were getting me great results and even made some regrettable comments on social media on how much more useful specific automations were compared to agents.

I feel silly about how little I actually knew.

It turns out that a good AI agent, one that understands your goals, your principles, your processes, and your work, is way more useful than I’ve admitted in the past.

So here, I’ll show you how I’ve set up my Notion workspace over the past 2 months to get the most out of Notions AI.

It crushes dynamic work. Meaning work I don’t have a process for yet. And it does that by knowing by business well enough to do work even better than I can prompt it to do.

All for $30/mo

Why I switched to Notion AI

I was using ChatGPT for the most part, otherwise scraping together pieces from different tools to get work done.

CustomGPTs, projects with specific context, saved prompts I can copy over etc..

Then, randomly, I saw someone’s tweet saying how much better Notion AI is now. It was something I had completely forgotten about despite using Notion every single day. Now I’m an advocate.

If you’ve ever used Cursor to vibe code, Notion AI reminds me a lot of that. You have all these folders and files, and the AI module on the left of the screen searches through all your connected assets to find relevant context anytime you give it a prompt.

I immediately saw the value and established 2 goals for what I wanted Notion AI to be able to accomplish for me:

1. It should be able to complete any part of a task that doesn’t require a deliverable outside of Notion (to at least 80% completion).

2. It should be able to complete all my sales admin: Update my CRM based on conversation history, create next steps for deals and put them in my tasks db, and write proposals all based on playbooks I’ve agreed upon.

Today, it does all this. Sure, there’s room for improvement. And these workflows are still in development as well. But I’m incredibly impressed.

I’ll have more specific examples of what kind of tasks Notion AI completes for me later on.

To reach these goals, I focused on Context Engineering.

Context engineering explained

Here is a ChatGPT provided definition of context engineering:

Context engineering is the discipline of designing, shaping, and controlling the context in which an AI system—especially a large language model (LLM)—operates, so it consistently produces useful, accurate, and aligned outputs.

Think of it as the next step beyond prompt engineering — instead of writing a single clever prompt, you engineer the entire environment (data, memory, constraints, tools, goals, and feedback loops) that the AI reasons within.

This is the AI version of having business documentation and standard operating procedures in place.

No more creating projects and uploading pdfs in ChatGPT.

No more engineering long structured prompts.

Often, I enter a one sentence (sometime misspelled) prompt, and an hour of work just disappears.

The AI agent knows, for example, that I always want to start with the smallest iteration of a project and test it. Thus, it always proves its work reflects that core principle.

It knows that I want everything documented, so when I ask it to complete at task, it first creates a task, uses a documented task creation SOP to fill it out, completes the task, then moves the task to “Done”.

It knows that it is taking part in the context engineering process, so when the completion of a project justifies new documentation, it creates the documents, and puts them in the right area.

The more I work with it, the better it’s own performance.

My Notion structure

I have five basic areas: Home, Strategy, Growth, Product, and Ops. In the marketing related stuff, it’s going to look through the Growth section. For tasks and organizational items, it looks through ops. Strategy is my North Star area that keeps us aligned on direction and goals.

Every area is structured the same way:

• Overview and strategic things it should know

• Playbooks and SOPs

• Quick links to assets and databases

This repetition is intentional. In every area, the AI knows what’s a guiding policy versus what’s a specific step by step path.

It’s very simple, and very effective.

Here is a look in my Ops space:

The Constitution

The most important asset is the Constitution. This is set as the system prompt, so it reads this every single time I give it a query.

The constitution includes:

• Our mission & vision

• Principles (how we work)

• Operating System

• Roles - what I do vs what the AI does

• Important business metrics

• Notion Workspace Schema

• Change log - if we change the constitution, what we changed.

I don’t know if it’s perfect, but it works well and keeps the agent focused on how I want all work done.

Note: the first sentence is an instruction to not change anything in my workspace without explicit approval. I highly recommend included this so the agent doesn’t move around in wrecking ball fashion.

Task SOP - my most useful piece of context

To give an example of an incredibly useful piece of context, I use my Task SOP multiple times a day.

It’s a simple document.

It outlines how I want all of my tasks laid out.

• How the title should be phrased

• Establish the core goal/challenge first thing

• Provide all the context related to the task (communications with other parties, related documents in my workspace, clients, meetings, seriously anything related)

• A definition of done - so the deliverable is clear

• Then a section labeled notes, that I drop information in while completing the task

This is an incredibly useful way to lay out all your tasks. It squashes any confusion, and gives anyone who looks at the task enough information to complete it with minimal additional questions.

The problem, of course, is I rarely ever fill out a task with this much rigor.

Notion’s AI agent has solved this problem in its entirety.

Now, when I create a task, I write a 3-4 sentence description of what I’m thinking for this task. Then, I tell the agent to fill out the task based on the Task SOP.

It takes my description, searches the rest of my workspace for context, and fills out the task near perfectly. Now, despite my lack of rigor on the small things, if you came into my workspace, you would know what’s being worked on and how to complete each task.

Here’s a recent example of a task to research video generation it filled out on my behalf:

You might be thinking to yourself, “But wait a second Adam. You’re a solo operator, a lone wolf, why do you need your tasks written out? Can’t you just remember them?”

Great question! You’re too smart for your own good.

You may or may not have noticed that a well written task is eerily similar to what a well written prompt looks like.

It’s not for me, it’s for the Agent.

The first step after checking its work on the task is the simple prompt “complete as much of this task as you can, and put the results in the notes.”

And boom! 80% of the task is done. Leaving me to make some small decisions and put it in whatever format I want the deliverable.

The meta part

A smart person, like you, would ask at this point, “Ok. but how long did it take you to set this all up?”

Here’s the thing: I did not create these assets. The AI created these assets. Strange world, I know.

I know what I’m going for in the structure and what I want on each of these documents. Then I just tell the AI to create the draft of this playbook or SOP. It knows what a playbook is because I created the constitution, which was also AI generated. Obviously I edit everything.

The AI created the context environment with my direction.

I’m just talking with the AI saying, hey, I want to create this kind of documentation because I think it’ll be good for the context you have available. This is my goal of this documentation. Here’s the things I think will be in it. And then, boom, I have a constitution, or a playbook, or a new database.

I had all of the versions of these documents up in a day or less.

Then, over the weeks, I’m telling the AI to change it.

One time, I had it create a task and the task title was very fluffy and vague.

I said, “hey, that task title sucks. I want you to forever create task titles in this format. Please go back and change the task SOP so that it reflects that.”

Making these changes to your context becomes super lightweight and easy because you get to just tell the AI to make the change. And now each time the output is improved because the AI is helping you develop the context that will improve and make it better.

Last trick - Whisper Flow

Last trick. I use WhisperFlow which lets me hold Fn to record my voice, then release to paste it wherever my cursor is. This makes the cycle really fast.

I think only half my typing is actual typing at this point.

The other half I’m just sitting here holding Fn, talking to my AI and watching it do a bunch of work.

I can rattle off a whole essay of my thoughts and it takes all that, changes my context, changes my workspace, or completes the task.

Conclusion

I can think of any set of tools that have actually changed the way I work more than the few I’ve mentioned in this article.

I’m not typically a tool junkie, so I’d be with you if you’re skeptical of that actual productivity add in this system. But I’ll keep you updated. Working well for me so far.

You don’t want an everything agent.
Most of your results can come from a few simple automations.

If I’m exaggerating, it’s not by much. And it’s understanding this basic 80/20 within AI that will stop you from drowning implementing this new tech, that’s still basically brand new, in your work.

The practical framework below comes from building lots of very real automations and systems using AI.

So let’s go over how to build simple AI automations that get results and actually reduce the amount of time you work instead of increase it.

1. Scope Clarity

The fastest way to build a great automation with AI is to shrink the problem until it’s easily solvable.

It’s cheating. But it always works.

The everything agent that knows every part of your business sounds magical.

I found this automation on Reddit. I bet the person who build this was thinking “I want to build an automation that runs my whole business”.

The reality is maintaining this automation is now this persons full time job. They may or may not ever get to running their actual business.

Instead, focus on making the scope narrow and testable.

Define the single outcome.

What is the “done” deliverable?

Bad Example: “Read and reply to all my emails using company knowledge”

Good Example: “Read emails from active clients, if response is needed, look through client project documentation to find answer or add task.”

Describe success in plain language.

Write a quick test to measure the results against.

Maybe take a manually created deliver from the past (an actual example of how you responded to client question about project status) and use that as a rubric to measure the AI against.

Example: “Given an email from an active client, we pull live project facts and produce a reply that cites timeline, blockers, and next step.”

Identify the inputs (Context Required)

Where is the truth? (CRM, Airtable, Gmail labels, Notion, Asana, ClickUp, etc...)

Manage your clients & projects in Notion? Then that’s where you will task the Agent with looking.

Let’s be honest… Are you actually putting all the info about your client and project there? Or is only some of it there? The AI output will fall to the quality of the Inputs.

List the process steps

Filter incoming email based on Active clients → Summarize the client request → Decide where to look for answers, based on workspace documentation (Client docs, client projects, client comms, etc..) → Write email draft answering client question.

Example reframes

• ❌ “Read my emails and handle whatever is needed.”

• ✅ “When an email from an active client arrives, fetch their current project row and draft a reply using live dates and status, then save as Gmail draft.”
  

• ❌ “When New Lead Comes In, Research The Company”

• ✅ “When new lead comes in, find the number of employees, when they raised their last financing round, at what valuation, and identify if they are currently regularly posting social media content, running ads, and writing blog posts. Based on that info, chose one of my three qualified ICP categories they fall into if any.”

At the end of the day, building automations IS engineering. And engineering doesn’t work in generalities.

You don’t build theoretical bridges. You build real ones.

2. Delete

Once you are clear on the scopes of the automations you want to build, ask yourself whether or not this automation and/or each of its steps should really be built.

And do this at every level.

Whether or not to automate a thing is a business strategy question.

For example, before you build a responding to client email automation you may want to ask yourself “Do I really want to provide my clients 24/7 email support?”

Are you sure it’s not better to just give your clients live access to their project board?

If yes, then ask yourself the same thing with each step of your proposed automation.

Do you really need to reference past client comms?

Watch out for is edge cases.

“90% of clients buy package A, but 10% have package B. And package B will have different steps to the automation.”

These kinds of forks are dangerous. They are innocent enough at the beginning, but as things grow they create a lot of problems.

There are 2 ways to handle this:

1. Generalize the automation enough to handle both cases in the same workflow.

2. Delete package B and be grateful your business in simpler now.

Apply “Minimum Necessary Context.”

More context ≠ better answers.

Large, messy inputs create contradictions and drift. Only give your prompts the context required to accomplish the task.

Don’t be KPMG:

Why? Because AI performance declines the more context you give it.

It’s called context rot.

So really ask yourself if all the context you intend to give your AI automation is really necessary, or if you can limit it in some way to only what matters.

3. Automate

Once you’ve broken things down to their smallest-useful-workflow, it’s then time to automate it.

Note: You won’t have simplified things completely, even though you tried, and that’s ok. It is often easier to simplify while revising the automation than while designing it. Chin up.

Another Note: I use N8N for my automations and I’m very happy with it. I’ve tried most of the other options and just pick the one you like working with the best.

Here are some rules I follow while building automations

Human in the loop by default.

Drafts saved, posts queued, statuses set to “Needs Review.”

Don’t let AI publish or email externally without your approval. Especially early on.

After you’ve seen tens or hundreds of cases, and worked out all the errors, you can consider this.

Or identify the cases in which AI can send the output live or send to you for review.

You can do this by either:

1. Have completion steps that are one step before pushing live: Write an email draft instead of send email, or put in Notion with “needs review” status instead of publishing.

2. Using in built human in the loop steps in N8N (or similar)

Don’t Use AI Where You Don’t Need To

If you don’t actually need AI for a step. Don’t use it.

For example, don’t have an AI agent search for client docs if you already know the client and can just run the search directly to Notion before hand.

It only invites room for error.

Code is deterministic. If conditions are met it produces the asked for result every time.

AI is probabilistic. If conditions are met, it will still fail sometimes.

Test your prompts on the front end

Once you publish your automation, you’re not going to want to continuously edit it. You just want it to work.

So make sure you really test your prompt.

Check how it handles the different inputs.

Make the adjustments on the front end.

Measure it’s performance against the deliverable standard you set at the beginning of this process.

Error notifications on

This is a simple hack.

Make sure you are looking at your error notifications from your automation tool, and debug you automation.

Set up error messages in Slack or where ever you will see them.

For some inspo: this automation creates all my content for me.

It’s not the most beautiful automation out there. But it’s mine and I love it.

Over the weekend it wrote over 600 social media posts for me.

4. Connect

Don’t build big workflows.

Build small ones and connect them.

Building small automations is easier. It also means your automations are more durable. And that makes your system as a whole more durable.

When each automation is stable (low errors, high success), here is how to link them together in a coherent system.

Trigger chaining (sequential)

Essentially, workflow A finishes → it triggers Workflow B.
And example might be an automation that puts new leads in your CRM, notifies you in slack, and then triggers another automation that gathers research on the lead.

Most automation platforms like N8N or Zapier will have the option to trigger another workflow and pass data into it.

The separation of these automations gives you more use cases.

Maybe most of your leads come from TypeForm. So the first automation adds them to the CRM.

But what if you meet a new lead at a conference?

Because the research automation is separated, you can create a different form for a different scenario that will still trigger your lead research automation.

(like texting an agent the lead info or something.)

Manipulating a common database

Automations that change information in the same database are inherently connected.

They are generally triggered when data hits a certain state.

For example, my content automation runs when I have new source content with the status “In Que.”

And they generally end by changing data in the database. Like changing a record status to “complete”

Where you can build another automation that looks for records with that status and do whatever with it.

This is the simplest way in which your automations will be connected, especially if they are all in the same area of your business - For example, all your sales automation will probably change data in your CRM and be connected that way.

Why this is a better system design than a big automation

This makes your automations way easier to debug. Failures are way easier to identify.

If you’re business changes and you have to pivot your process around, you won’t lose your whole automation.

It’s also way less intimidating. You can make improvements one workflow at a time.

Conclusion

When I follow these principles, I end up building useful automations:

• Scope Clarity gives me outcomes I can test in minutes.

• Delete removes as many chances of failure and chaos before starting. Also just don’t build things I don’t need.

• Automate with a human-in-the-loop keeps me safe while I learn edge cases.

• Connect turns tiny reliable pieces into a system that scales with me.

Hope this was useful for you. Feel free to reach out and let me know your thoughts!

My dad is a general contractor in LA. He just finished a $20M building and wanted more projects.

We set up an AI that controls my computer and researches leads for him. One week later, they had more to bid on than they could handle, each bid in the $10–$50M range.

How we got there was actually pretty simple.

1. Fix the sales environment

2. Create a new source of leads

3. Task AI to find the permits and extract lead info (to qualify the project and get contact info)

Once the basics were in place, the AI was easy. Here’s the breakdown.

1. Fixing the sales environment

Their “CRM” was a local Excel file bounced back and forth over email. ~2,000 leads with chaotic fields: duplicate columns, statuses spelled three different ways, notes in address fields, addresses in status fields.

Filtering was basically impossible.

What I changed:

1. Moved to Airtable. Spreadsheet is a mess. I needed a database and the ability to create views for different phases of the process.

2. Data repair with scripts. A couple Python scripts did the heavy lifting:

   1. Consolidated duplicate fields and picked the best-value data when conflicts existed.

   2. Normalized statuses (e.g., LEAD / Lead / lead → Lead).

   3. AI prompt that took each records JSON, made sure the fields had the right info, and returned an updated JSON with corrections.

3. Interfaces for each stage. Broke the sales process into stages (New Lead, Qualified, Called, Bidding). Then I built views or interfaces for each stage so each process was silo’d and nobody was messing up each others data.

4. Call queue for one person. To make this dummy-proof for my 70 year old father (bless his heart) I made him an interface that ONLY showed the next lead he needed to call and had ONLY the information necessary for him to call.

Switching from “stare at a sheet of 1,000 rows and figure out what to do” to “press call on the next record” took them from a call every few days to 2–3 calls per day. That’s a ~6x action rate improvement before we touched lead volume.

This might feel incredibly basic (because, of course, it is!), but it’s important because later we are going to us AI on a bunch of data. And if you’re data is not organized and structured like my Dad’s wasn’t it’s incredibly difficult to do any kind of automation, let alone AI automation.

2. A better lead source

Builders rely on directory-style “job boards.” They’re expensive, inconsistent, and crowded. Basically all the builders in your area are also getting those leads.

So we needed our own lead flow.

We looked for Ready-To-Issue (RTI) properties on LoopNet. Projects where a developer has land & approved plans and is now selling that as a package.

Whoever buys that RTI property will build soon. So we want that lead as soon as it’s available

The flow:

• LoopNet filter → email alerts.

• AI reads each alert email from LoopNet, extracts the unstructured description, fills a JSON “lead card,” checks for duplicates, and creates the lead in Airtable.

• A small check runs later to see if an RTI listing disappears (likely sold), the status changes in AirTable and it’s ready for the next step.

This works because we know someone is buying that property to build it. But they haven’t open the project up for the rest of the contractors yet. So we can be there early and build a relationship.

Developers like this because they really need estimates to map out their financing before they get hard bids from builders. Us being there to give those estimates gets us a lot of face time on these big projects.

But we are not there yet.

3) Using Browser-Use to get the permit info.

Why permits? Permits tell you if a project is actually moving. For LA, a BLDG-NEW record is a strong buying signal. It effectively means they are telling the city they’d like to get started building.

Permits also give you a lot of lead info and often list owner/filers, contacts, and sometimes links to plans.

That said, LA’s permit system (ZIMAS) isn’t bot-friendly. Parameters don’t yield stable deep links, forms must be filled in anew, and records are… creatively duplicated (the same address can show up multiple times with different fragments of history).

So I couldn’t build a generic scrape. I needed to use AI.

What we did:

1. Created a Python environment (using UV) to run this project.

2. Got the Browser-Use repository set up.

3. In a single script pulled the relevant leads from AirTable

4. Started a for loop that took these steps.

5. Structured prompt with relevant data from record.

6. Ran my prompt using Browser-Use

7. Updated the AirTable record with info from permit.

Here is the prompt we gave browser-use

''' I'm going to give you an address, I need you to go to the Los Angeles City permit records website and find the latest BLDG-NEW permit associated with this address. 
    This is the address: {address}

    Here are the steps you will need to take to get the most recent permit. 

    1. go to zimas.lacity.org

    2. on the search pop-up, put the house number in the house number field, and the street name in the other field. Make sure to follow the formatting directions stated on the website. 

    3. click "Go"

    4. Once the address is found, on the left sidebar, click the drop down "Permitting and Zoning Compliance"

    5. In that dropdown, click "View"

    6. On the new screen, click the plus icon to expand the "Permit Information Found:" section

    7. If multiple addresses are showing, expand ALL of them.

    8. Find the BLDG-NEW permit with the most recent date in the "Status" field across each address.

    9. Click the link in the "Application/Permit#" section of that record

    10. Return the URL of that page and the date of the permit application
    '''

And this worked out pretty well. It would either not return the link because it didn’t exist, or it did return it but the most recent one was still years ago, or it would return it and it was recent.

That link would get brought into our AirTable where we could grab the rest of the info when we needed.

And that’s pretty much it. This was the largest bottleneck in the sales process. Once we automated it we had deleted the work that took up 80% of our time. What was left was incredibly easy.

Notes from the build:

• Smaller models struggled with the unpredictable UI states (cookie banners, shifted menus). A larger reasoning model handled it reliably.

• It’s slower than a human on a single record, but it runs 10–12 hours straight. One 12-hour run cost about $200 in tokens and was absolutely worth it given deal values.

• After tuning, we went from 1–3 new leads/day to spikes of 24 qualified/day (we paused the system once they had 10 serious bids in motion).

Thoughts

It was pretty cool to build an AI automation that took over the computer and did lead research.

I don’t know if I would do it for most use cases.

It worked here because we were able to really eliminate and define what step of the process was costing the most time.

In other words, we found the bottleneck and it was solvable.

I’d do it again if it called for it.

Three times over the past year (as new generations came out), I tested the usual suspects to see which model writes the best short-to-medium content using my prompts and method.

The tests weren’t lab-grade. No crazy rubric. Just the same prompts, lots of outputs, and a practitioner’s eye for what breaks in real workflows.

What kept surfacing: larger OpenAI models follow instructions best, respect the source, and leave me with the least editing. That matters more than “creative voice.”

Context

My prompts are 2–4 pages. They include:

• Example Post to mirror tone, hook, length, and format.

• Target Audience so the piece lands where it should.

• Source Material that the model must stick to for facts and framing.

• Instructions to glue it all together (structure, cadence, sectioning, constraints).

This isn’t zero-shot “go write something.”

It’s a lightly sophisticated prompt with many moving parts and points of failuer. I’m optimizing for instruction following and source faithfulness. It is much easier to add personality and flair to content than it is to restructure it.

Here is the prompt that I most recently tested.

You are an expert content and social media creator.

Write a <Platform> post that matches the GOAL, STYLE, TONE, LENGTH, READABILITY, and PUNCTUATION of the CONTENT_EXAMPLE, using the SOURCE_MATERIAL as the single source of truth, and is maximally relevant to the TARGET_AUDIENCE.

Matching The Content Example
I'm giving you a CONTENT_EXAMPLE that performed very well on <Platform>. Analyze that piece of content and understand why it performed well. 
Is it making a polarizing claim and then giving an unpopular opinion? Directly solving TARGET_AUDIENCE's pain point? etc...
First make a central claim that has the same tone and goal as the CONTENT_EXAMPLE, but uses the Source Material is the inspiration for the post. 
Mirror cadence, sentence length, heading/bold usage, list style, and spacing of CONTENT_EXAMPLE.

Using the Source Material
The SOURCE_MATERIAL is the inspiration for your new post. It is what your new post is ABOUT.
Although the post should read just like the CONTENT_EXAMPLE, the SOURCE_MATERIAL should guide what's in the post.
You do not need to RIGIDLY COPY the SOURCE_MATERIAL. 
You can make opinionated claims that do not appear in the SOURCE_MATERIAL if it helps you match the CONTENT_EXAMPLE better. 
But do not state an action, process, results, or metrics as facts if they do not directly appear in the SOURCE_MATERIAL.

Aligning the Post For The Target Audience
Identify how the SOURCE_MATERIAL is relevant to the TARGET_AUDIENCE based on pain point and desired outcomes. 
Let that guide how you shape your central claim to be relevant to both the SOURCE_MATERIAL and the TARGET_AUDIENCE. 
Continue to make the post about solving the pain point and achieving the desired outcomes of the TARGET_AUDIENCE within the bounds of the content example framework. 


Copy Editing Considerations
Follow the principles of Hormozi's Content Method (Hook, Promise, Reward)
Review your work to make sure you have follow this method as well as you can while staying within the instructions given above.
Hook - Needs to grab the readers attention and get them to stop scrolling
Promise - Needs to convince the reader to read the whole post
Reward - Needs to fulfill the stated promise. 

Prioritize the Reddit Snippet
The Title and the first 6-7 lines are what a reader can see before deciding to click the post. So those parts need to be compelling enough to get reader to click on the post. 



Inputs

CONTENT_EXAMPLE = """<paste example post>"""

TARGET_AUDIENCE = """<your audience snippet>"""

SOURCE_MATERIAL = """<your source>"""

Silent Consistency QA (do not print)

Before returning the post, silently verify and revise if needed:

Give yourself a score out of 10 on how well you followed each instruction. If below a 7 on anything, make the necessary changes to get above a 7.


Return only the final post.

1. Gemini Seems To Get It… and Disagree

Gemini understands the brief, but tends to do it’s own thing anyway.

• It recognizes the ask but wanders on structure and formatting.

• It tends to default to a safe, moderately formal tone.

• It uses the source material… but not the content example constraint as tightly as needed.

I would frankly rather it hold tightly to the content example, even if the wording sounds weird because it’s not quite a fit.

Otherwise, I have to spend my editing time re-imposing my structure into the post.

Verdict: Good comprehension, weak obedience. If you value predictable formatting and example-mirroring, you’ll be herding cats.

2. Claude Has an Attitude

Claude’s voice is great. Its judgment can overrule your brief.

• Most readable, “human” tone.

• But when my instructions collided with its preferences, it adjusted the deliverable on its own.

• It respected the spirit, not the letter, of the brief.

Really it’s much like Gemini, but with more fun and casual writing.

Verdict: Best raw voice, not the best follower of specific detail inside your prompt.

Note: Claude still my favorite for coding.

3. Open Source: I Wanted It to Win

I use a lot of tokens for content (21M in the last 14 days). I wanted open source to work. Would save me $100+ per month

I tested locally with the biggest I could practically run (e.g., Llama 3.1 70B). Two problems:

1. Throughput: Too slow to use. Reasoning models are generally better so I could have used a deep-seek model. But with thinking it would just be incredibly slow.

2. Capacity: Even at 70B, it struggled to juggle all the instructions + example + source content.

For content that must follow a complex prompt and not hallucinate beyond your source, you need more capability.

For internal tasks that other people don’t see, I could see myself using open source.

5) OpenAI (and Why the Biggest Model Wins)

Across versions, one pattern was stable: the bigger the OpenAI model, the better the content quality for instruction-heavy prompts.

This is probably obvious, but the bigger the model the better it performed. And for content you intend to publish, that really matters.

So I’m not recommending openAI models, I’m specifically recommending GPT-5 with the additional parameter of “high” thinking effort, which basically increases the amount of thinking tokens used for each prompt.

Why it’s better:

• Instruction adherence: It mirrored the content example I gave it ruthlessly. Even if it meant the posts didn’t exactly make sense. That might sound like a weird thing to want, but it’s way easier for me to edit those posts.

• Source faithfulness: It successfully derived all substance of the post from my original source content. Didn’t make up anything.

• Editability: Even when the style is a touch “plain,” the structure is correct. So improving is fast.

Is it worth saving money on the smaller models? No. It would cost me more time spent editing. If OpenAI had a model that was twice the size and twice the cost of GPT-5 I would probably use that one instead.

The End

For now, I’m sticking with OpenAI’s biggest model for content, specifically GPT-5 with high-effort thinking. Because it follows instructions and doesn’t make up new stuff.

Voice is easy to add. Structure is expensive to fix.

Introduction

These are the mistakes I’ve made while publishing 100+ AI-generated posts.

I built automations to write for ~30 channels. Some posts went nuts. One cracked 900,000 impressions, a handful crossed 100,000, but most sit at 1-2K impressions. Classic Pareto.

Along the way I’ve been making crucial mistakes that killed performance. It took a lot of experimentation to really even understand that I was making mistakes to begin with.

But with every fix, I saw huge improvements in the quality of content I got with my prompts.

Use this as a checklist. Tighten it once, benefit for months.

For context. I laid out my entire system on this post:

I’ve changed things a little bit since then, like moving to N8N instead of Zapier. But the basic structure still applies.

1. Trying to scale too quickly despite bad prompts

I hit a few early wins and tried to expand the whole system overnight.

10–20 channels, 10–20 pieces per day.

Without testing how my prompt behaved across different cases.

The result was a flood of mediocre drafts, that on the surface made no sense.

I polluted my dataset with context that was too abstract and ultamitely unnecessary.

And spent way too much time editing bad content I generated instead of fixing the structure of the prompt to produce better content.

Why it kills performance

• With AI automations the bottleneck automatically becomes whatever part of the process requires human effort. In this case editing. So any decreased performance in the AI means huge human costs at the bottleneck.

• Editing at scale replaces creation with cleanup. You’re not compounding proof—you’re compounding rework.

The fix - Way More Upfront Testing

• Different Prompt Structures. I came up with 4 or 5 ways to structure the prompt and tested each one with different sets of context to see what worked best.

• Different Types of Content. I tried different kinds and lengths of content (will get into more below).

• Different Process. Tested between splitting up prompts into multiple steps, zero shot prompts, two-shot, many shot.

Payoff

The payoff here was huge as it really reduced the human bottleneck requirements for each output. In other words, it takes a lot less time to make good content now.

2. Abstracting instead of using examples (Prompts and Content Guidelines)

I tried to reverse-engineer what “works” by abstracting from multiple posts and turning that into a meta-prompt + channel guideline. It felt rigorous. It produced mush.

And mush really is the best word to describe it I think.

For example, I would find 5 posts that performed well on the target channel, and abstract out why that post performed well.

Why did the headline work? What is it about the intro?

Then I’d turn that into a set of instructions in the prompt.

It was a mess.

Why it kills performance

• Too abstract. It made my prompts too specifically vague. And ya I know that doesn’t make sense. Lots of contradictions the AI handled in a generic way. Making all my posts even though they were different prompts, look the same.

• Because different variables were both abstractions (My content prompt and the channel guidelines) there were contradicting instructions within the context. This killed my outputs.

The fix - Use single examples and minimum necessary context

• Instead of giving an abstracted instructions on a post type, give it one example post to follow.

• Minimize the rest. I replaced channel guidelines with a short target audience snippet that only included a description of the audience, a couple of pain points, and a couple of dream outcomes.

Results

This made the outputs much higher quality and way more predictable.

It also had the added benefit of making it way easier to create new content prompts. All I need to do is give it different content examples to get different kinds of posts.

Examples:

Bad

I need you to write a post that I’m going to publish on Reddit.

I will give you source content to base the post on. From this source content you should get all the information that will ultimately go into the post.

I will also give you an example post. This example post performed very well on Reddit, so I want you to understand why it worked and then generate a post that will perform similarly, but on whatever topic the source content covers.

Here are limitations you must explicitly follow (taken from the example’s metrics):

Length & structure
Total length: 110 – 120 words (the example is 114).
3 short paragraphs after the headline:
Story setup ≈ 60 words
Key take-aways ≈ 35 words
Engagement question ≈ 9 words
Headline
Single line, ≤ 10 words.
Combine a concrete number with a strong emotional verb (e.g., “Lost $5,800 …”).
Sentences & formatting
Max 2 sentences per paragraph.
No sub-headers inside the post.
Keep formatting minimal; line breaks move the reader along.
Voice & tone
Casual, matter-of-fact, humble.
5th-grade reading level; common, small adjectives.
No forced humor, clichés, or over-drama.
Content integrity
Rely only on details in the source content.
Do not invent numbers or facts.
Purpose of each paragraph
Para 1 → Paint the painful/lucky lesson with vivid but concise context.
Para 2 → Deliver 3-5 actionable take-aways the reader can copy immediately.
Para 3 → End with a direct question that invites the subreddit to share its own stories.
You should begin by studying the example post and the commentary below, then strategize how to apply the source content so it matches the feel, structure, and arc. After strategizing, generate the post.

Example Post with Commentary
Headline (Shock & Proof of Stakes)
Lost $5,800 Building an AI Agent for a Client

Commentary:

 Uses a hard number (“$5,800”) + a negative result (“Lost”) to spark curiosity and establish real stakes in only 9 words.

Story Setup (Painful Lesson in Context)
Hey r/AI_Agents, wanted to share a painful lesson. I've been developing AI agents for customer service and project management (built some cool Jira integrations) for a while now. Recently, I spent two months creating a custom agent for what seemed like a legitimate startup. After delivering the final product, they completely ghosted me – taking $5,800 of unpaid work with them.

Commentary:

 61 words. Opens with a friendly greeting, frames the post as a “lesson,” and supplies just enough back-story to prove expertise and make the loss feel tangible.

Key Take-Aways (Actionable Advice)
For fellow freelancers: always use contracts, insist on milestone payments, thoroughly research clients, trust your gut feelings, and include kill-fee clauses. Don't let excitement over cool tech cloud your business judgment like I did.

Commentary:

 35 words. Bullet-like list compressed into one sentence delivers immediate value; humble admission keeps the writer relatable.

Engagement Question (Invite Community Response)
Anyone else been burned? What are your protection strategies?

Commentary:

 9 words. Ends with a direct, open question, prompting comments and boosting engagement.

{{Rest of prompt}}

Good

You are an expert content and social media creator.

Write a Reddit post that matches the GOAL, STYLE, TONE, LENGTH, READABILITY, and PUNCTUATION of the CONTENT_EXAMPLE, using the SOURCE_MATERIAL as the single source of truth, and is maximally relevant to the TARGET_AUDIENCE.

CONTENT_EXAMPLE = """Everyone asks how I make $10K/month alone.

Here's the uncomfortable truth they don't want to hear:

You need to fire 80% of your current clients
You need to charge 5x what you think you're worth
You need to say no to most opportunities
You need to focus on systems, not hustle
You need to think bigger than trading time for money
Comfortable advice keeps you broke.

Uncomfortable action makes you rich.

Repost this if you know someone building a one-person business.

They'll thank you later."""

{{Rest of Prompt}}

3. Letting contradictory instructions happen in large prompts

My content prompts are maid up of multiple variables that get added in as context.

One mistake I was making was letting contradictory instructions happen in those variables.

This goes back to the concept of “minimum necessary context”.

In my case, I had a content prompt (specific instructions on the post I wanted), and channel guidelines (what kind of content works on this channel).

And in those two assets there were regularly contradictions.

Why it kills performance

• Conflicting rules push the model toward generic defaults.

• You think you’re being thorough. You’re actually forcing it to reconcile competing instructions. It basically tries to play this safe.

The fix was giving each variable a smaller scope

• No context overlap. Each input asset has one job:

  • Example Post: concrete style anchor.

  • Audience Card: pains & desired outcomes only.

• Removed possibility of conflicting instructions. By switching to a content example and a target audience snippet, there was not possibility of conflicting instructions.

• Local Examples Only. Before I would use the same content example across different channel I felt it would match with. I stopped doing this just to enforce as much alignment as possible with my content example and target channel.

Payoff

This improved my outputs a lot. And it actually made each output distinct and unique. It got rid of the mush. The AI wasn’t just writing the same generic version of my source content over and over again for slightly different channels.

In other words, saved me a lot of time editing and improving content.

It also reduced the amount of input tokens per prompt and saved me some money.

Here’s one of the channel guidelines I was using before:

# Guide to Creating High-Performing Content for r/socialmedia



## Introduction

This guide aims to equip you with the necessary elements to turn video transcripts into high-performing posts suitable for the r/socialmedia subreddit. It is informed by successful posts that engage the community by delivering value, insights, and actionable advice.



## Who is our audience?

The r/socialmedia subreddit attracts a diverse audience, ranging from social media professionals to casual users looking to grow their presence. Here's an overview of the primary audience:



### Audience Demographics

- Social Media Managers: Professionals managing social media accounts for businesses or clients.

- Influencers & Content Creators: Individuals looking to grow their personal brand or monetize their online presence.

- Entrepreneurs & Small Business Owners: Business owners seeking to leverage social media for customer acquisition and brand awareness.

- Marketing Enthusiasts & Analysts: People interested in understanding social media trends, algorithms, and best practices.



### Audience Interests and Goals

- Growth Strategies: Effective methods to increase followers, engagement, and reach.

- Algorithm Insights: Understanding how social media algorithms work and how to optimize for them.

- Content Creation Tips: Best practices for creating engaging content.

- Case Studies and Success Stories: Real-world examples of social media strategies that worked.

- Monetization Strategies: How to generate income from social media platforms.

- Platform-Specific Advice: Tips for Instagram, TikTok, LinkedIn, Twitter, YouTube, and Facebook.



## What They're Looking For

- Actionable Strategies: Step-by-step guides on improving engagement, reach, and conversions.

- Insider Knowledge: Insights from social media professionals and experienced creators.

- Algorithm Breakdowns: Deep dives into platform updates and how they affect growth.

- Case Studies and Examples: Success stories that provide real-world applications of growth tactics.

- Engagement Opportunities: Content that sparks discussions and allows for community feedback.



## Content Best Practices



### 1. Make It Actionable

Posts should provide practical steps that users can follow to improve their social media presence. Example: “10-Minute Tweaks to Improve Your Social Media Today.”



### 2. Use Data & Insights

Social media changes constantly. Posts that include recent algorithm changes, engagement data, or industry insights perform well. Example: “I Interviewed 3 Instagram Employees & Studied 2,000+ Posts – Here’s How the Algorithm Works.”



### 3. Provide Templates & Resources

Sharing downloadable templates, content calendars, or analytics tracking sheets is highly valuable. Example: “Here’s a Social Media Report Card & Google Sheet I Use for Clients.”



### 4. Share Personal Experience & Results

Users appreciate real-world applications of strategies. If you’ve grown an account or implemented a new tactic, share your journey. Example: “How I Grew to 100K Followers in 30 Days.”



### 5. Encourage Discussion & Engagement

Posts that ask questions or invite users to share their experiences perform well. Example: “What’s Your Most Successful Social Media Growth Hack?”



### 6. Break Down Platform-Specific Strategies

Different platforms require different approaches. Posts that analyze how to optimize content for Instagram, TikTok, YouTube, etc., get high engagement. Example: “Instagram vs. TikTok Growth – What Works Best in 2024?”



### 7. Debunk Common Myths

There’s a lot of misinformation about social media. Posts that correct misconceptions attract engagement. Example: “Going Viral Won’t Change Your Life – Here’s What Really Matters.”



### 8. Make Use of Lists & Step-by-Step Guides

Readable, scannable content performs best. Example: “20 Instagram Story Ideas You Can Use Today.”



### 9. Provide Monetization Tips

Many users want to know how to turn their following into income. Posts covering affiliate marketing, sponsorships, and digital product sales perform well. Example: “How I Made My First $1,000 on Instagram Without a Huge Following.”



### 10. Stay Up to Date

Social media is constantly evolving. Posts that cover recent updates, trends, and new platform features drive engagement. Example: “TikTok Just Changed Its Algorithm – Here’s What You Need to Know.”



## Post Formatting & Style

- Engaging Title: Make the title clear, concise, and intriguing.

- Intro Paragraph: Start with a strong hook that explains why the post is relevant.

- Bullet Points or Subheadings: Break up text to improve readability.

- Personal Examples or Case Studies: Add credibility by showing results.

- Conclusion & Call-to-Action: Encourage users to share their experiences or ask follow-up questions.



## Conclusion

The r/socialmedia subreddit thrives on actionable insights, real-world experiences, and engagement-driven content. By following this guide, you can create high-performing posts that educate, inspire, and engage the community effectively.

You can see there are instructions that go into how to write posts. But there were already specific instructions on the post I wanted written.

Now I only provide this Target Audience Snippet:

# Social Media Marketing Professionals

A community of social media marketing (SMM) professionals, managers, and enthusiasts who share industry knowledge, discuss best practices, and give constructive feedback on marketing strategies. They are focused on growing and engaging audiences across platforms, from aspiring social media managers to seasoned digital marketers.

##Pain Points
- Constant algorithm changes: Frequent platform updates disrupt strategies, as tactics that worked last year can suddenly flatline in reach after new algorithm tweaks.

- Heavy workload & multitasking: Many social media marketers juggle managing accounts, creating content, engaging with followers, and analyzing metrics single-handedly, often with minimal support – leading to stress and overwhelm.

- Content creation burnout: There’s pressure to continuously produce fresh posts instead of repurposing proven content, causing wasted effort and creative fatigue as managers “reinvent the wheel” daily.

- Low organic reach: Marketers struggle with weak organic engagement, finding it frustratingly difficult to grow communities without resorting to paid ads to boost visibility.

- Lack of organizational buy-in: Social media efforts are often undervalued internally, forcing SMM professionals to fight for resources and constantly prove their ROI with data to justify campaigns.

##Desired Outcomes
- Clear growth strategies: Established, step-by-step social media frameworks that balance trending content with evergreen posts for sustainable long-term growth.

- Adaptability to change: Agility in adjusting to new features and algorithm shifts so they can “adapt and thrive in this evolving landscape” without losing momentum.

- Stronger organic engagement: Steady audience growth and active community interaction that allows brands to reach more customers and build loyalty without heavy reliance on paid advertising.

- Efficient workflows: Greater use of automation and smart tools to streamline repetitive tasks (like scheduling and basic interactions), saving time and energy for strategy and creative work.

- Proven ROI and impact: The ability to track and demonstrate concrete results (followers, traffic, leads, sales) from social campaigns, giving evidence that their work drives business growth.

No more conflicts with the instructions on what post I want written.

4. Breaking the prompt into multiple steps with edits after each one

I tried a two-stage pipeline:
Stage 1: generate a channel-agnostic draft → human edit.
Stage 2: revise for each channel → more edits.

I thought by doing a first round edit before the post was duplicated and then editted for 4-5 different channels, I would save editing time.

This was not the case. Just more editing.

Why it kills performance

• Each stage reinterprets intent and style and introducing drift.

• The edits I made before passing it back to the AI for channel based revisions were basically just undone and the post made generic again.

The fix was to fix the fundamentals

• Better prompt structure. Basically all the fixes I’ve talked about already. Focused on making a better smaller prompt instead of more stages and complications.

• Two Shot Prompt. I now, in 1 automation, have another prompt that runs on the output that tells the AI to review it’s adherence to instructions in the last prompt and make necessary revisions to the output.

Automation Examples

Original bad process:

New Cleaner Process:

The details are not that important. But the thing to notice is how much cleaner the second version is. About 50% reduction in steps.

5) Giving the prompt unnecessary context

This is the theme of mistakes 2 & 3, but deserves its own section.

I over-stuffed context. At the time I think it seemed easier. Now I understand that most of building an AI system is actually managing your context.

What context not to include is just and important as what context to include.

Why it kills performance

• Contradictions. As I’ve mentioned, this brings in contradictions that confuse the AI and defaults it to generic outputs.

• Context Rot. There is a diminishing return on input tokens. At first more context improves performance, then there’s a point where more context worsens performance.

The fix here is minimum necessary context

• Switching to Content Example. Providing one example to follow and no additional writing instructions. From the channel I’m targeting so there is inherent alignment.

• Audience Snippet. This was me really narrowing down what I needed the AI to know about my target audience. IDK how token I reduce but it certainly reduced the contextual load.

Payoff

This didn’t only make things easier on the AI but it also made my whole content production system lighter and simpler.

Adding new channels and new prompts will forever be easier because I nailed it down to only the necessary context.

The chart below shows how model performance declines and context (input tokens) increases.

6) A bad “first edit” humanizing prompt

I added a third pass. A “humanizer” prompt to remove clichés and tidy phrasing. It felt clever. I thought it would reduce editing time. It actually flattened voice and made everything same-y. More AI did not fix AI.

Why it kills performance

• A generic humanizer normalizes style across posts, erasing the example’s distinct cadence.

• It often re-introduces the clichés you were trying to remove (“In today’s fast-paced world…”).

The fix

Basically all the things I’ve mentioned so far. This was basically a remnant of the legacy process I had that was bad.

Humanizing prompt:

Rewrite the following text to make it sound as if a human wrote it, in a direct, first-person tone. The voice should be that of a credible, no-nonsense expert sharing hard-won advice with peers. It should sound confident and grounded, not overly enthusiastic or reliant on hype.



Write from the perspective of an independent growth hacker who does marketing experiments and shares them with the community for the sake of getting input, feedback, and generally helping his peers.



Use everyday language instead of formal or overly professional phrasing. Try not to sounds too robotic.



Mix up the sentence lengths and structures. Some short and punchy, others longer and more complex. Combine or split sentences if you need to improve the flow.



Avoid conversational cliches, filler words, and clickbait-style lead-ins. For example, do not use phrases like "The crazy part?," "Here's the thing," "You won't believe this," or "It's a game-changer." The impact should come from the substance of the information, not from linguistic tricks.



Write with clarity and precision. The tone should be approachable, but the focus must remain on conveying practical information directly. Prioritize strong, specific verbs and concrete details over hyperbolic or vague language.



Keep all the original information and meaning intact. Do not remove any important details or change facts. Try to keep the same structure, format, and look of the post as well. 



Keep in mind that this is for Reddit and it needs a subtle title that pulls readers in.



Look to keep it more concise than the original if possible.

CONTENT:{{Post}}

Snapshot of my content generation prompt now.

Inputs (each with one job):

1. Source Content
   My transcript/notes. The “what.” This ensures originality.

2. Audience Snippet

• Pains - 3 bullets on the problems this audience has.

• Desired outcomes - 3 bullets on the goals this audience has.

3. Example Post (Channel-native, previously high-performing)
   One post that already won on this channel. That post encodes length, cadence, formatting, and the unspoken rules.

In other words. Much simpler.

Full Prompt

You are an expert content and social media creator.

Write a Reddit post that matches the GOAL, STYLE, TONE, LENGTH, READABILITY, and PUNCTUATION of the CONTENT_EXAMPLE, using the SOURCE_MATERIAL as the single source of truth, and is maximally relevant to the TARGET_AUDIENCE.

Matching The Content Example
I'm giving you a CONTENT_EXAMPLE that performed very well on Reddit. Analyze that piece of content and understand why it performed well. 
Is it making a polarizing claim and then giving an unpopular opinion? Directly solving TARGET_AUDIENCE's pain point? etc...
First make a central claim that has the same tone and goal as the CONTENT_EXAMPLE, but uses the Source Material is the inspiration for the post. 
Mirror cadence, sentence length, heading/bold usage, list style, and spacing of CONTENT_EXAMPLE.

Using the Source Material
The SOURCE_MATERIAL is the inspiration for your new post. It is what your new post is ABOUT.
Although the post should read just like the CONTENT_EXAMPLE, the SOURCE_MATERIAL should guide what's in the post.
You do not need to RIGIDLY COPY the SOURCE_MATERIAL. 
You can make opinionated claims that do not appear in the SOURCE_MATERIAL if it helps you match the CONTENT_EXAMPLE better. 
But do not state an action, process, results, or metrics as facts if they do not directly appear in the SOURCE_MATERIAL.

Aligning the Post For The Target Audience
Identify how the SOURCE_MATERIAL is relevant to the TARGET_AUDIENCE based on pain point and desired outcomes. 
Let that guide how you shape your central claim to be relevant to both the SOURCE_MATERIAL and the TARGET_AUDIENCE. 
Continue to make the post about solving the pain point and achieving the desired outcomes of the TARGET_AUDIENCE within the bounds of the content example framework. 


Copy Editing Considerations
Follow the principles of Hormozi's Content Method (Hook, Promise, Reward)
Review your work to make sure you have follow this method as well as you can while staying within the instructions given above.
Hook - Needs to grab the readers attention and get them to stop scrolling
Promise - Needs to convince the reader to read the whole post
Reward - Needs to fulfill the stated promise. 

Prioritize the Reddit Snippet
The Title and the first 6-7 lines are what a reader can see before deciding to click the post. So those parts need to be compelling enough to get reader to click on the post. 



Inputs

CONTENT_EXAMPLE = """<paste example post>"""

TARGET_AUDIENCE = """<your audience snippet>"""

SOURCE_MATERIAL = """<your source>"""

Silent Consistency QA (do not print)

Before returning the post, silently verify and revise if needed:

Give yourself a score out of 10 on how well you followed each instruction. If below a 7 on anything, make the necessary changes to get above a 7.


Return only the final post.

Conclusion

And that’s it. Since making these systemic improvements I’m able to get a lot more content per hour of editing time.

Taking the time to fix the design of the process really made a huge affect.

Last year, I quit writing content.

I was exhausted from continuously writing content using what I thought was a good system. Day after day of showing up and being a good consistent boy.

A therapist could not have been more direct than my X analytics. “This isn’t for you, Adam.”

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My process was what you’d expect if you’re following the advice of creators on X and LinkedIn who create content about being a creator.

I thought of ideas, sometimes wrote pillar content, pulled that into as many small tweets, medium size tweets, threads, monologues, carousels, etc… Then published.

But nothing.

So I stopped. Got some space from the issue like my analytics was always trying to tell me to do.

Started doing cold outreach, and after hundreds of hand typed messages I got lucky and landed a small client.

The work? More content. But for them I had an idea. I would take their youtube content, and use AI to repurpose it for all niche communities online relevant to their topic.

It worked surprisingly well.

After that I thought, I can probably do this without all the hard work of producing youtube videos. All I really needed was a transcript or a big piece of source material. In fact, loom gives you the transcript of all your videos.

I tried it out, filmed a 20 minute loom that was a case study of my campaign, ran it through all the prompts, published it on a bunch of Reddit channels, and got 120K impressions.

Back in the game.

More importantly, I was getting leads from my content.

There are 2 core insights to this new process:

1. Make producing content way easier on yourself.

2. Publish it where it actually has a chance to succeed.

And so here it is.

The Strategy: Distribution is everything

My content on X and LinkedIn didn’t work because nobody followed me there. The best I could do was get a couple hundred impressions on a post.

At first I believed what the meta-creators told me: It’s your content, stupid.

As soon as I psychologically dug my way out of that loop, I realized it wasn’t my content. And I should of known. Every business strategy book, and all my experience and study in marketing was telling me the whole time: It’s your strategy, stupid.

I opted out of posting on my own socials, they are the wrong channel. NO ONE IS THERE! of course it’s the wrong channel.

Instead I researched where my audience actually is. Subreddits, X communities, LinkedIn Groups, Newsletters, etc…

But each of those places are different. And getting even more specific, each subreddit is different. I can’t just repost the same post over and over again.

That’s where the new content production system comes into place. I need to produce content efficiently for all these micro channels where my audience actually is.

My core assumptions were:

• Most of these communities are free and easy to access.

• If a post looks and feels like it belongs, it'll get noticed for the value it provides, not as being a promotion.

• Just like normal social media, a small percentage of readers will go to my profile and check out my links.

• AI could repurpose my pillar content to be great fits for each individual channel.

• Even if only a handful of posts took off, the overall effort would be worth it.

Every step was designed to test these assumptions.

Note: this began on subreddits, and I’ll be soon testing these assumptions on the other channels mentioned earlier.

The Playbook: How I Did It

1. Create the Pillar Content

I recorded a 20-minute Loom video walking through a specific marketing process. I had an outline, and it was basically just a step by step, ‘how I built this campaign’. I did not take this video too seriously. It was not meant to be watched by anyone. I sound monotone, take massive pauses throughout, and generally look stupid. Then I got a full transcript of the video.

2. Build a Swipe File

This was the most important part. For each platform where my audience hangs out (I mean specific subreddits) I spent time researching. I found the most successful posts in each community by sorting them by "Top of All Time”.

There was similarities in the content of similar subreddits of course, so if a post typed worked for two subreddits, I noted that.

3. Decode What Works

I analyzed those top posts. What was the hook? How long were they? What was the tone? The goal was to break down each post well enough that I could create a prompt (next step) that would take my source content and create a very similar kind of post with it.

4. Craft Custom AI Prompts

I used those guidelines to write specific prompts for my AI tool. I was very direct.

Here’s an example of a prompt I'd use for Reddit:

I need you to write a post that I’m going to publish on Reddit (or a similar founder-friendly forum).

I will give you source content on any topic. From this source content you should pull every fact, statistic, or anecdote that will appear in the post.

I will also give you the Example Post below. Study it first, then mimic its structure, pacing, and tone when turning the source content into a brand-new post on the new topic.

Hard Limits
Word count: 360 – 400 words (the Example Post is 382 words in total; stay within ± 20).
Headline length: about 19 words (Example Post headline = 19 words).
Max 3 sentences per paragraph; leave a blank line between paragraphs.
Do not add sub-headers, bullets, bold, italics, or any fancy formatting—plain text only.
Voice: friendly, matter-of-fact, lightly self-deprecating, 5th-grade reading level, minimal jargon.
Stick to facts in the source content; no invented numbers or drama.
Use common, simple adjectives (“big,” “small,” not “colossal,” “jumbo”).
End with a short invitation to ask questions or get help.
You should begin by reviewing the Example Post, then outline how you’ll map its arc onto the new source material, and finally generate the post.

Example Post with Commentary & Word Counts
Headline (19 words | Purpose: Hooks with drama + credibility)

 After Google Killed Our First SaaS, Here’s How My Wife and I Launched Our Second One—With No Coding Experience

Commentary:

 Promises a failure-to-success story, names a famous antagonist (“Google”), signals low-code approach, and builds intrigue about a couple founder team.

Introduction (16 words | Purpose: Sets context & timeline)

 Four months ago, I shared the launch of our first SaaS, Huxley, here on this subreddit.

Commentary:

 Immediate time anchor and social proof (“shared here”) pull the reader in.

Scrappy Build Phase (44 words | Shows resourcefulness)

 It was our first real attempt at building something, and even though we had zero coding skills, we dove in using WeWeb (frontend) and Xano (backend)—the simplest tools we could find to get started. My wife handled the backend, I worked on the frontend.

Commentary:

 Details the no-code stack plus humble competence; builds relatability.

Original Value Prop (52 words | Explains initial idea)

 The idea behind Huxley was pretty simple: use Google’s Indexing API to help people speed up page indexing. Normally, Google Search Console limits you to indexing 10 pages a day manually. But with the API, you could do up to 1,000 a day! Perfect for people with tons of pages to index.

Commentary:

 States problem, constraint, and 10× solution in plain language.

Risk Acknowledgement (42 words | Foreshadows tension)

 Now, we knew from the start that building a business entirely dependent on an API was risky—APIs can change or get shut down anytime. But we went for it anyway because we needed a project to learn and get our hands dirty.

Commentary:

 Shows self-awareness; primes reader for coming failure.

Failure Climax (43 words | Drama peak)

 Fast forward a month: we launched Huxley and somehow got 4 paying customers on day one. It was amazing… for a week. Then Google announced they were limiting access to their Indexing API. Just like that, our 7-day-old SaaS was dead. That hurt.

Commentary:

 Rapid success-then-collapse amplifies emotional stakes.

Pivot Moment (19 words | Turns story around)

 After that, we took some time to regroup and think. And now, we’re back with a new project: Magritte.

Commentary:

 Signals resilience and sparks curiosity about the new idea.

New Problem Statement (47 words | Relatability)

 This time, we wanted to solve a problem I personally struggled with: coming up with good ad creative ideas. As a marketer, I know how hard (and time-consuming) it can be to come up with ads that actually work. So we built Magritte to make that easier.

Commentary:

 Personal pain point makes the solution credible.

Feature Teaser Line (4 words | Segues to details)

 Here’s how it works:

Commentary:

 Simple bridge line keeps momentum.

Core Features (32 words | Demonstrates value)

 We’ve curated 3,000+ proven, high-converting ad creatives from real campaigns. You can filter the library by brand, topic, industry, business model, performance, and more—so you’ll find exactly what you need in minutes.

Commentary:

 Concrete numbers + filtering power convey depth.

Ongoing Momentum (37 words | Shows growth)

 On top of that, we’ve turned 150 of the best creatives into pre-designed, customizable templates. You can tweak them to fit your brand and launch your ad in minutes. And we’re adding 50+ new templates every week.

Commentary:

 Weekly cadence signals commitment and future value.

Call to Action (19 words | Engages community)

 If you’ve got questions about Magritte, feedback, or even just need help with anything SaaS-related, feel free to ask!

Commentary:

 Opens conversation and invites engagement.

Friendly Sign-off (8 words | Closes warmly)

 I’d be happy to help however I can.

Commentary:

 Ends on generosity; leaves door open.

Use this breakdown and the word-count guide above to craft the new post from any topic I supply.

5. Generate and Refine

I fed the transcript and my custom prompts into the AI. I have something like 10 prompts for 10 channels, some prompts can be used for multiple channels. So I make about 30 posts per piller content.

6. Edit

The outputs are basically scaffolds of your pillar content put into the write format, sequence, length, etc.. It is really not publishable from a writing perspective. I take the post into Gemini and open up a canvas. I finish the editing there.

7. Publish

I try to post at optimal times, following all community rules. Then I stuck around to reply to comments and answer questions. The goal was to be a genuine participant, not just a content-dropper.

8. Capture Leads Naturally

Instead of a CTA at the end of the post, I optimized my profile. My bio was a one-liner explaining what I do and a link to my site. The idea was to let people who found the content valuable come to me.

9. Track and Adjust

In my AirTable where I manage my content I backfill the impressions each post got and keep track of which communities I get the most traction in or which prompts produce the highest performing content.

The Results

• My average post gets 5-11K impressions

• A high performing post gets 40-90K impressions

• Content is way easier now

• Having fun again

Final Thoughts and What I Learned

The biggest takeaway is that customization is everything. A single, generic post is worthless. A dozen posts, each tailored to a specific audience, is a powerful distribution engine.

The research part, figuring out what works in each community and creating essentially post templates, is the most crucial step. It's the difference between a post that gets deleted and a post that goes viral within that niche.

If you're thinking of trying this, just start. Record a short video today, find one or two relevant communities, and spend 30 minutes creating a custom prompt. Then post it and see what happens.

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So what will?

That’s the question I’m chasing.

And I found 2 ideas so far, that seems to be a step in that direction. These are less step by step instructions and more strategic frameworks for you to bring into your mix, and apply to your specific situation.

I’ll break down the strategic thinking behind these approaches, and the basics of how to apply them.

Here’s a sneak peak of the strategies:

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The New “Free Consultation": Imagine an AI-generated, 15-page custom growth plan landing in a prospect's inbox on your first outreach. The kind of deliverable that might take 10 hours of work, delivered for free with no strings. That would make a prospect think about you.

• Scott Galloway did the analog (actual work) version of this strategy to close Yamaha and Levi’s at $500K/yr each… As his first clients out of college.

• Ripple saw 100% increase in ROI of their cold email campaigns with this very strategy.

Micro-Channel Expansion: Posting more, lower quality, tweets on your profile with no followers will do nothing. But what if you can expand your distribution to 40+ channels with built in audiences of millions?

• For one client we got 1M impressions on a single post in r/wallstreetbets that brought in 20+ customers

• That same client had an average of 70K impressions per post using this strategy. Compared to 2K per post on their company socials.

• Personally I’ve had marketing posts do 80K impressions and bring in 5 leads. No social following to speak of.

The Challenge: Cutting Through the Noise

The B2B marketing landscape is louder than ever. Cold email volume has increased by 73% this year. It feels like AI Marketing 101 has become using some copycat of AiSDR to read your prospects LinkedIn posts and think of a witty personalized message to get a conversation going.

On the content front, the situation isn't much better. Just last week a prospect sent me an essay they had “written” with a sense of accomplishment. They will publish that among many other posts on their website and wonder why the marketing results they’re after are not being met. Slowly, reports are coming in about dramatic increases in the amount of posts being posted on social media thanks to AI.

From Efficient to Effective: The AI Paradigm Shift

Focusing on efficiency, like generating posts or personalized outreach messages, might offer temporary competitive advantage. But these advantages can be replicated by competitors over night. What was originally incremental increases in ROI become status quo.

From the perspective of a marketing strategist, the real goal is to find new ways to be effective with AI. This means using AI to create value in ways that were previously impossible or prohibitively costly. It's about shifting from doing the same old things a little faster, to doing entirely new, high-impact things that truly move the needle. This approach allows you to stay ahead of the curve for much longer, building a moat around your marketing efforts.

Our core challenge became clear:

• How could we use AI to deliver a 10x value jolt upfront to make cold prospects genuinely eager to engage with us?

• How could we place that value where competitors weren't flooding the feed, and do it without adding to our budget?

Strategy 1: The New Free Consultation (Product + Price)

This strategy is all about delivering immense value upfront to a select group of high-value prospects. Instead of a generic cold email, you're delivering a highly personalized, high-value asset that demonstrates your expertise and understanding of their specific needs. A good pretext to this is the Dream 100 campaign by Chet Homes.

While each deliverable might initially take 15-20 minutes of human time per prospect, the perceived value and conversion rates make it incredibly worthwhile. As you refine the process, you'll find ways to optimize and save even more time.

The 6-Step Process for High-Value Deliverables:

1. Pick a High-Value Deliverable: Identify a deliverable that you would typically provide to a client, which is perceived as highly valuable and directly relevant to their specific situation. This could be a custom growth plan, a detailed market analysis, a personalized audit, or a strategic roadmap. The key is that it's about them and provides actionable insights.

2. Identify Necessary Data & Information: Determine precisely what data and information you need to assemble this offering for each prospect. This might include public company data (revenue, funding, employee count), industry trends, competitor analysis, recent news, or even specific pain points derived from their website or social media activity. Think about what you would normally ask on your discovery or onboarding call.

3. Build Your Data Pipeline: Leverage tools like Clay, ChatSheet, or similar data enrichment platforms to source the identified information. Automate the process of getting this data into a structured format.

4. Create Prompts or Prompt Chains: Develop a series of prompts or a sophisticated prompt chain for whatever LLM. These prompts will instruct the AI to take the collected data and transform it into a draft of your high-value deliverable, following a predefined structure and tone.

5. Automate the Drafting Process: Integrate your data pipeline with your LLM through automation tools like Zapier (or Clay again). Effectively automating that once the necessary points of the data pipeline are filled out, it will automatically generate the draft deliverable.

6. Human Editing & Continuous Optimization: The AI-generated draft provides a significant head start, but human oversight is crucial. Spend time editing and refining each deliverable to ensure accuracy, polish, and a bespoke feel. As you go, continuously look for ways to optimize the entire process, to save even more time per prospect.

The fundamental principles in play with a strategy like this are pretty simple. Going back to the 4Ps of marketing, this is an attempt to give you prospect the most Product you can for free (low Price).

If you can build the process so that the end result is in fact a deliverable your client will want, it will succeed.

Strategy 2: The Micro-Channel Expansion (Promotion + Place)

This strategy is about finding untapped audiences where your competitors aren't. While using AI to bring down the cost of producing content for these smaller channels.

This takes ‘Place’ of the Ps to the extreme by essentially going to the smallest channels you can promote on, where there are virtually no large competitors to speak of, and making them profitable by also taking Promotion to the extreme using AI to diminish the cost of producing new promotional material.

I’ve run this playbook for a client and averaged 73K impressions and 2-3 customers per post. I’ve also run this for myself with an average of 11K impressions per post and a lead every other post. It works.

The 6-Step Process for a Micro-Channel Blitz:

1. Research Possible Channels: Identify and research niche communities that align with your business. look at platforms like Reddit, Facebook Groups, LinkedIn Groups, X communities, and niche forum sites like IndieHackers, or whatever is relevant for your niche. For each potential channel, build a list that includes its audience size (e.g., members, followers) and an assessment of the content quality. Your goal is to find engaged communities with a clear theme that you can add value to. Probably start by picking your best 3 or 5 and build from there.

2. Create Your Pillar Content: Instead of starting with a blank page, I recommend filming a loom of your pillar content. This gives you a transcript you can put in your prompt and have repurposed for the channels you are targeting. If you already have some form of pillar content like newsletter posts, or podcast, great. Use that.

3. Find Model Content for Each Channel: For each channel you are targeting, find 3-5 really great posts that you like and that performed well on that specific platform. Analyze what makes them successful—is it the tone, the format, the use of visuals, or the way they engage with the community? Then, create a prompt that will take your pillar content transcript and transform it into a post that resembles your model post, ensuring it's tailored to the community's unique style and rules.

4. Edit Drafts in Canvas: Take the AI-generated output and bring it over to the Gemini Canvas. Use Gemini to help you edit the post for humanness, removing hyperbole and clichés. Often, you can highlight sections and give instructions on how to change entire parts of the text. This process significantly reduces the overall editing time and makes the final polish more enjoyable.

5. Post and Optimize: After editing, post your content. This is where you learn what works and what doesn't. There are a few points of failure that can happen here—your content might not be a great fit for the channel, the channels you picked might not be as great as you thought, or your prompts aren't producing decent content and the editing time is too high. All of these issues can be fixed over time as you look at your results and optimize each piece of the process, from channel selection to prompt engineering.

6. Automate and Scale: To scale this process, you want to automate the flow of taking your pillar content and turning it into first-draft content for all your channels. I personally use AirTable and N8n for this process, which allows me to connect my content source with my AI prompts and scheduling tools. I'll be detailing that workflow in another post, but the key is to build a system that minimizes manual work, allowing you to focus on strategy and high-touch engagement.

I recommend starting with text content. I have not move to multi-media which I know is going to be a more complicated process for carousels and videos. But that is probably where this is going.

The End

I hope you were inspired by these examples. Remember not just to look at these campaign ideas, but the process of going back to marketing first principles and stretching to figure out how the new tools available allow you to really change the playing field.

There is a lot of undiscovered opportunity beyond the tactics I laid out above.

Share Adam K. Stinson

The dream is getting all the customers you need through organic content. I'm mean seriously, it's peak business positioning. By never having to pay to acquire more customers, your business has a moat like no other.

The problem is, getting their is a long term investment that isn't straight forward. If growing a newsletter with X or LinkedIn content was easy, you wouldn't be reading this.

In this article, I'm not going to show you how to solve this challenge. I'm going to show you how to go around it completely.

The idea for this marketing strategy came to me when I was helping a client. Like many companies they were struggling to profitably acquire customers. They had a YouTube channel that was bringing in all their customers but it stopped growing. They hit a plateau. They were losing customers at the same rate they got new ones.

In their analytics, I saw Reddit was the 4th largest source of traffic even though they paid no attention to the channel.

Eventually, I thought up the idea of using AI to produce unique content for every relevant subreddit we could find.

The results were great:

• Over 4 million impressions on Reddit

• 70K Impressions Per Post on Average

• Hundreds of new user sign-ups

• Significant increase in website traffic

• Reduction of customer acquisition costs from $300–$400 to approximately $80–$100 per paying customer

Since working with them, I've improved the strategy to be about 3x more efficient than it was.

In this comprehensive guide, we'll delve into how we achieved these results, sharing each step of the process so you can replicate this strategy for your own marketing efforts.

Diagnosis of the Challenge

Before diving in, I want to go over the challenge this particular strategy solves, and what challenges we will face in the implementation.

The Retail Marketing Problem

Advertising is essentially paying retail prices for you customer acquisition. It's the most expensive way to get more customers. At a fundamental level, it is profitable if there's enough demand for your product to make up for paying retail prices for customers.

If your advertising is profitable out of the gate, you have Product Market Fit. Congrats. If not, demand is usually increased through branding.

Building owned media channels like social media accounts, newsletters, YouTube channel, etc... Is like manufacturing your own customer acquisition. Your per unit cost will be much cheaper than advertising. But the capital expenditures happen at the beginning of the process.

In other words, you are investing more capital on the front end, for cheaper customers later on.

Earned Media, on the other hand, is like bartering for your customers. Earned media is when you get your content, or content about you, on somebody else's owned media. You get the benefit of cheap customer acquisition of owning a channel, without the initial investment.

The catch is, you have to give the owner something they need (content that will also help their ends), and play by their rules.

Reddit is Different

From a marketing perspective, Reddit is more like earned media than social media.

A bunch of other people are building communities.

A bunch of other people are supplying the content in those communities.

Building your own profile simply does not matter. You don't own the media. Unlike other socials, your very first post can perform just as well as your 1,000th post.

Because the fundamentals of the platform are different from other socials, the rules are much closer to earned media than social media. You have to follow their rules. And top among those rules, on virtually every subreddit, is no self promotion.

This usually turns a lot of founders away. But the reality is, this is how ALL earned media works. You can't put your Call To Action at the bottom of the Forbes article you're getting. When Time magazine features Elon Musk, there is no link to buy a Tesla.

Earned Media is about the story. It's promotion, not advertising.

The Economics of Earned Media

How the difference of earned media shows up in the metrics is much higher impressions (more views) and much lower click through rate.

Where an Ad or even an email sent to your list may have a 1-2% click rate, earned media is closer to 0.2-0.5%.

The good thing about this is you get more branding for your money from the higher impressions (demand generation). But to get the volume of conversions you need, you'll need 2-4x the volume of content.

This is where the AI comes in. By using AI to the highest degree that's technically possible, we can get a 10-20x improvement in the cost of writing content. Which you'll need to make Reddit, or earned media in general, profitable.

But we're not talking about copying and pasting prompts into ChatGPT. The whole content production process needs to be automated to the fullest extent possible. And well.. It's not like AI content is all that great.

The Guiding Policy

The solution to all these challenges was building a mostly automated system that produces highly quality posts for each subreddit, aligns with the expectations of the community, while making our product part of the narrative of the post.

And so the strategy was built on the following pillars:

1. Leverage Existing Content: Utilize a piece of Pillar Content, like a YouTube video, to feed in the AI prompts so the AI doesn't make up random or generic information.

2. AI-Assisted Content Creation: Develop a system where AI generates drafts of subreddit-specific posts, significantly reducing the time required for content creation.

3. Human Editing for Quality Assurance: Employ human editors to refine AI-generated content, ensuring accuracy, clarity, and community alignment.

4. Subtle Brand Integration: Include understated references to the client's product, such as watermarked charts within posts, to pique interest without triggering community backlash.

Step by Step Campaign Build

In this section, we'll walk through the entire process step-by-step, providing a detailed guide on how to implement this strategy for your own marketing efforts.

Quick note: I built this whole system in AirTable with N8N running all the automation.

Subscribe to this newsletter and you'll get:

1. The AirTable Database Template

2. The N8N automation

3. 3 Prompts you can use for the campaign development

And It will save you about 80% of the time it takes you to build this campaign.

1. Identifying the Right Subreddits

Objective:

Find all the subreddits where your target persona is likely to be active.

Steps:

1. Generate a list of Key Words to search on Reddit to find the right subreddits

Generally I start with the persona's I'm targeting. Then, for each persona, I would generate a list of key words.

Example: Digital Marketers

• digital marketing

• marketing

• social media marketing

• email marketing

• facebook advertising

And just go through that process for each persona.

You can also use a simple prompt to get a list from ChatGPT

Get Prompt Here

1. Search Each KW in Reddit and Click over to Communities

Obviously you'll see a lot of options pop up here. Right out of the gate you can ignore the communities that are obviously not relevant.

Put these channels in the channel table in AirTable. Include basic info like member size and the link. Ultimately, you will build a database of channels that looks like this:

How do you know which subreddits are worth while? I do a quick evaluation on these 3 items

• Relevance - Is it about my topic?

• Size - Does it have enough members to matter?

• Content - Will I be creating the kind of content that performs well on this channel?

If you just getting started, start with 5-8 subreddits you want to target. For context, I'm currently generating content for about 60 myself.

2. Researching and Analyzing Top-Performing Content

Objective: Collect the highest performing content on each channel to base a content guidelines document off.

Steps:

On each Subreddit change the filter from "Hot" to "Top", and then a time filter will pop-up and you need to change that to "All Time"

This will show you the highest performing posts ever posted on that subreddit. These will shape the content we make for the subreddit. Look for posts that will make a good foundation for content guidelines.

For example, memes should be excluded. Grab the posts like look like content you can produce and will serve our other objectives.

Copy these posts in a Google Doc and separating them by subreddit.

3. Creating Channel Guides for Each Subreddit

Objective: Develop a guideline that encapsulates the style, tone, and content preferences of each subreddit.

Use the content as the basis of creating a subreddit content guidelines

Now that you have all this content saved, we need to study it and look for specific attributes to create a content guideline.

Here is the basic outline of the content guidelines we're looking to create.

Content Guidelines Outline:

1. Introduction

2. Who is Our Audience?

2a. Audience Demographics

2b. Audience Interests and Goals

3. What the Audience Looks for in Content

4. Key Elements for Successful Posts

5. Writing Style & Tone

6. What to Avoid

7. Conclusion

You can use AI to write all of these for you to save a bunch of time.

The prompt I use is included when you subscribe to the newsletter

Add the content guidelines to the content guidelines field of each channel in your AirTable.

Here is an example content guidelines for one of my channels

4. Crafting the AI Prompts

Objective: Create comprehensive prompts that instruct the AI on the specific of the post we want it to create.

If you were paying attention to the high performing posts you found on each channel, you found that different kinds of posts perform well on each channel.

Some of the high performing posts may have been tactical break downs of how the author accomplished a specific thing, others will have been big questions about the niche topic, others list style posts like "6 things I learned after 10 years in niche".

We differentiate between what kind of post we want the AI to create in the prompt. If you have the AirTable you'll see there is a prompt table where all these prompts are stored.

Put Collected Posts into different buckets based on post type

Now that you have a collection of posts, start to categorize them based on the kind of post they are. I recommend doing this in a new tab on your Google Doc.

The title of each post is usually the give away to determine what kind of post it is, so you can use that as your short cut.

I have tried to feed all the posts to ChatGPT and get it to do this part but it does a horrible job.

One post type at a time, create a prompt that will get the AI to produce a new piece of content like the ones in your post type bucket

I used ChatGPT to do this, and you can get the prompt I used by subscribing.

Add to AirTable

In AirTable, add these outputs as a record in the Prompts table. Put the content in the prompts field.

Add a channel relationships to the prompt where this kind of post performs well.

5. Automate The Content Production Process

Objective: Automate running all the prompts to produce a lot of content.

Now that we have all the assets, we need to build the automation in N8N.

Here is the basic logic of the automation:

1. Trigger manually or on timer

2. Get source content records in source content table with status "In Que"

3. Get all Prompts in prompt table (with status "Active")

4. For Each Prompt Record, Run content creation prompt

5. If you have a lot of channel, take this output and create new content record with it. Edit this before triggering the revising of the content based on the channel guidelines. This will save you a lot of editing time.

6. For each prompt, get all related channel records.

7. For each channel record run prompt to revise content based on content guidelines

8. Create content record in Content Table with this output.

That's essentially how the automation works. If you have used AirTable the same way I have outlined until now, you can download my N8N automation and it should work once you connect all your credentials.

Subscribe to download the N8N automation.

6. Human Editing and Revision of AI Content

Objective: Refine AI-generated drafts to produce polished, publishable posts.

At this point, AI has taken a ton of the work off our plate. But the content is generally not quite good enough to publish. So we need to take the time to edit each post.

Steps:

1. Review for Tone and Style:

   • Adjust language to sound natural and authentic.

   • Remove clichés and overly enthusiastic expressions that may seem unnatural.
     

2. Verify Facts and Figures:

   • Double-check all data, especially numerical information.

   • Correct any inaccuracies stemming from transcription errors or AI misunderstandings.
     

3. Align with Subreddit Norms:

   • Ensure the content adheres to community guidelines.

   • Adjust formatting and structure as needed.
     

4. Finalize the Post:

   • Proofread for grammar and clarity.

   • Confirm that the content provides genuine value.

In AirTable, you can create an interface where you can filter your content records on status or publication date to edit to streamline your editing.

7. Subtle Integration of Product Mentions

Objective: Weave our company into the narrative of each post.

This is where we finally tie the post back to the actual thing we want to promote. There's a couple reasons we save this until the end:

1. We really want to emphasize that leaving mentions of our company is an afterthought.
   
   This is linked to the performance of the post. If your post comes off as self-promotion, it won't get upvotes. If it doesn't get upvotes, it won't get impressions and you risk getting booted off all these channels.
   

2. If you try to have the AI weave in your product mentions, it will absolutely butcher it. It does a very bad job with subtlety.

Here are some tactical approaches to weaving your company into the post narrative. Think of this like leaving a trail of breadcrumbs back to your website for those that are seriously interested.

1. Include Relevant, Branded, Visuals:

   • If you can bring in screenshots that are relevant to the post, and also highlight the product, this is great.

2. Contextual, Non-Hyperlinked, Mentions:

   • If in the middle of the post it makes sense given the content to mention your project, do it. But don't add a link. Give the reader what they need to go to Google and find you that way.

3. Point to other pieces of content:

   • If you can mention an article, youtube video, etc... that's contextually relevant, and at that location mentions your product, you can link to it. But be careful about this.

Rule:

Instead of doing the same thing each time, do whatever is best for that specific post. It needs to feel as natural as possible.

Key Insights To Keep In Mind

These are just some additional notes on things I ran into while running this campaign

• Space out your scheduling. You will have a lot of posts that are similar but not the same. Rotate channels and the prompts to have as much diversity on your profile as possible.

• Optimize your profile. Another great way to get people back to your website is by optimizing your profile.

Conclusion

That's it!

Alright obviously this is not a campaign for the light-hearted. But once you get it going, it works like a charm.

Remember to grab all the free templates associated with the campaign.

If you have questions, feel free to email me.

If you'd like me to run this campaign for you, schedule a call!

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Hey, I’m Adam Stinson—growth-marketer-turned-AI-tinkerer. I break down one practical playbooks that shows you how to let large-language models do the heavy lifting while you keep the credit (and the free time).

Expect:

• Step-by-step guides you can run in a lunch break

• Swipe-file prompts tested on real client campaigns

• Zero fluff—just ways to spend less time marketing and more time building

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Jokes aside — In 2024 a YC startup who became a client graced me with the opportunity to try something crazy.

The goal was to target niche channels that others would have considered too low ROI.

My plan was to build a system that used AI to automate content production for each channel.

It worked. Once scaled we were posting about 15 times per day on subreddits, X communities, and other similar places.

We were getting about 4 Million impressions and 200 users per month.

It’s no viral AirBnB-like growth story.

But it was that companies 2nd largest customer acquisition channel (as soon as we launched it!), and cost less than $30/mo to run.

So now I’m here, starting this newsletter where I will expand on this work, document it, and share it with you.

My first post will be a full breakdown of how you can run the campaign I just described for yourself.

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