Most people use AI like a faster typewriter. They prompt, AI outputs, they clean it up. Repeat. Every single time.
But here's what I've realized after watching hundreds of founders try to “leverage AI for productivity”: you're still the bottleneck. You're still required for every task. You haven't automated anything—you've just sped up your own manual labor.
And here's the thing most people miss: they're not just rushing through prompts—they're rushing through problems. They want AI to give them answers before they've fully understood the question.
I call this failing to marinate in the problem space.
Expertise doesn't come from quick solutions. It comes from sitting with a problem long enough to understand its edges, its exceptions, its traps. And automation—real automation—requires that you've done this work first.
What if instead of prompting AI, you programmed it? Once. With your expertise. In plain English. And then it executed complex workflows—the same way you would—without you being there at all.
That's what I did with my content creation process. It used to take me 4+ hours per article. Now AI runs 80% of it through 6 markdown files I wrote once. Let me show you how.
The Prompting Trap
Here's the thing most founders don't realize: every time you prompt AI, you're spending your time. You're loading context. You're making decisions. You're quality-checking output. You're fixing mistakes.
Sound familiar? It should. That's what you were doing before AI—just slower.
I spent three months watching SaaS founders adopt AI tools. The pattern was always the same:
Week 1: “This is amazing! I just saved 2 hours!”
Month 3: “I'm spending just as much time as before, just… differently.”
The problem isn't AI. The problem is how we're using it.
Think about this: every complex task in your business requires your judgment—your expertise on what good looks like, what decisions to make at each step, what pitfalls to avoid. When you prompt AI fresh each time, you're re-explaining all of that. Every. Single. Time.
That's not automation. That's supervision.
The Real Insight: Prompting vs. Programming
Here's what changed for me.
I stopped thinking of AI as an assistant I prompt and started thinking of it as a system I program.
The difference?
Prompting = telling AI what to do THIS time
Programming = telling AI what to do EVERY time
When you prompt, you're spending your time on each task. When you program, you spend your time once—encoding your expertise into instructions AI can execute forever.
And here's the beautiful part: you don't need to write code. You don't need to build an app. You need markdown files.
Markdown is the bridge. It's human-readable—you can write it in plain English. And it's machine-readable—AI can parse and execute it. Your expertise, encoded once, executed infinitely.
But here's what that requires—and this is the hard part.
You can't encode expertise you haven't developed. This method doesn't create judgment. It multiplies judgment you've already built by marinating in your domain. If you haven't spent time deeply understanding the problems you solve, you have nothing to encode.
I call this The Expertise Encoding Method.
The Expertise Encoding Method: Three Core Principles
Before I show you how to build this, you need to understand three principles that make it work:
Principle 1: Stage-Based Workflows
Every complex process has discrete stages. Content creation has stages. Sales has stages. Product development has stages.
Each stage has a clear input, a transformation, and an output. When you break your workflow into stages, you give AI a map—here's where we are, here's what we're doing, here's what success looks like.
Principle 2: Decision Logic in Plain English
Your expertise isn't just “do this, then that.” It's full of judgment calls: “If the topic scores below 4, pick a different angle.” “If the headline doesn't create tension, rewrite it.”
Write these as if/then rules any human could follow. AI can follow them too.
Principle 3: Examples That Teach
Here's what separates good encoding from bad: you show AI what good looks like.
Don't just say “write a compelling headline.” Show five headlines that work and explain why. Show three that don't work and explain why. AI learns by comparison, not instruction.
How to Build an Expertise-Encoded System
Let me walk you through the exact process, using my content creation system as the running example.
Step 1: Map Your Workflow Into Stages
Start by identifying the discrete phases of your process. For content creation, I identified 7:
- Audience – Who am I writing to?
- Ideation – What's my insight and format?
- Hook – What headline stops the scroll?
- Architecture – What's the structure?
- Draft – How do I write with psychological triggers?
- Personalize – What personal stamp makes this mine?
- Polish – Is it authentic and ready to publish?
Notice: these aren't vague phases. Each has a specific job. Each has a clear output that becomes the input for the next stage.
Step 2: Document What You'd Tell a Smart Intern
For each stage, write as if you're training someone capable but inexperienced.
This is where most people hit a wall.
To tell a smart intern what to do, you first have to know what you know. And sometimes, that's the hardest part.
The encoding process doesn't just transfer knowledge—it reveals gaps. You'll find yourself writing “if the topic doesn't fit, pick a different angle” and realizing: wait, how do I actually know when a topic fits? What signals am I reading? What patterns have I learned?
That's marinating. That's the real work.
Take my Audience stage. I didn't just write “figure out who you're writing to.” I encoded my entire micro-segmentation framework:
- How to score a topic against audience segments (1-5 scale)
- How to build goal pyramids from goals and pains
- What questions to ask: “Which goals does this topic connect to?”
- When to kill a topic (scores 3 or below everywhere)
- Example: Full walkthrough of scoring a topic
The file is 550+ lines. Why? Because that's how much expertise goes into doing this well. Expertise I built by spending years marinating in audience dynamics—not by reading a blog post.
Step 3: Add Quality Gates Between Stages
Before moving from Audience to Ideation, AI checks:
- Segment selected
- Micro-segment identified
- Goal pyramid built
- Topic scores 4+
- Angle is clear
If any of these fail, AI doesn't proceed. It flags the issue and waits.
Quality gates prevent garbage from flowing downstream. One weak stage ruins everything after it.
Step 4: Include Examples and Anti-Patterns
My Hook stage includes 8 headline formulas. But I don't just list them—I show examples:
Good: “How I reduced engineering turnover from 30% to 8% in 18 months”
Why it works: Specific numbers, exact timeframe, concrete outcome
Bad: “Leadership lessons I learned”
Why it fails: No specificity, no tension, no promise
AI learns from comparison. Show the contrast.
Step 5: Create the Trigger
How does AI know which stage to run?
In my system, AI reads the current state: Do you have an audience selected? Run Stage 1. Do you have a headline? Run Stage 3. And so on.
The files themselves contain the logic. AI doesn't need external instructions—the system is self-documenting.
Proof: This Very Article
Let me show you what this looks like in practice.
This article you're reading was created using the system I'm describing.
Stage 0: Audience
I fed the topic—”encoding expertise into markdown for AI automation”—to the system. It scored the topic against my 5 audience segments:
| Segment | Score |
|---|---|
| Scaling SaaS Owners | 5 |
| Digital Agency Leaders | 4 |
| Technical Founders | 3 |
| Engineering Managers | 3 |
| Enterprise Product | 2 |
Winner: Scaling SaaS Owners. Micro-segment: founders at $10K-$100K MRR who are still the bottleneck for complex processes.
Stage 1: Ideation
The system used the “Intersection Discovery” prompt to find insight: combining documentation, AI automation, and expertise encoding. Format: Framework Builder.
Stage 2: Hook
The system generated headline candidates using 5 different formulas. Winner: the contrarian position—”Stop prompting AI for every task.”
Stage 3: Architecture
The system built a complete outline using the Framework Builder structure: Problem → Insight → Method Overview → How to Build → Proof → How to Start.
Stage 4: Draft
The system wrote this draft, applying psychological triggers: Problem Agitation in the opening, Hidden Problem for the insight, Mental Pictures for the walkthrough, Authority Proof for this section.
Stage 5: Personalize
I added my “marinating in the problem space” metaphor—the thread you've seen woven throughout. That's the stamp that makes this unmistakably mine.
The result?
Before: 4+ hours of staring at a blank page, uncertain about angle, inconsistent quality between pieces.
After: 45 minutes of oversight. AI executes 80% of the work. Consistent quality because the system encodes what “good” looks like.
That's not a productivity hack. That's automation.
How to Start: Your First Expertise-Encoded Workflow
You don't need to build a 7-stage system on day one. Start smaller.
Step 1: Pick ONE workflow you repeat at least weekly. Documentation, research summaries, client onboarding emails, proposal creation—anything with repetition and judgment.
Step 2: Write out the stages. Most workflows have 3-7 natural phases.
Step 3: For stage 1 only, write what you'd tell a smart intern. Include the decisions you make, the quality standards you apply, examples of good and bad output.
Step 4: Run AI through it. Note where it goes wrong.
Step 5: Add clarification where it failed. This is the encoding—translating tacit knowledge into explicit instructions.
Step 6: Iterate until stage 1 works consistently.
Step 7: Move to stage 2. Repeat.
Here's the test: if you can't explain it to an intelligent person without experience, you can't encode it for AI. The encoding process forces you to articulate what you actually know.
That's valuable even if you never run AI through it.
The Real Unlock
Here's what I want you to take away.
The markdown files aren't the magic. The encoding process is.
It forces you to articulate expertise you've built over years of marinating in your problem space. It makes you confront what you actually know versus what you think you know.
Most founders can't automate their expertise because they haven't taken the time to understand it themselves. They jumped to solutions. They never marinated.
This method only works if you've done that work. But if you have? You can multiply it infinitely.
Start with one workflow. Encode it. Watch it run. Then encode the next one.
A year from now, you'll have a library of expertise—your judgment, your standards, your approach—that runs 24/7 without you touching a keyboard.
That's not productivity. That's leverage.
What workflow would you encode first? I'm genuinely curious—drop a comment or send me a message. I've been building these systems for months now, and I'm always looking for the next process to systematize.