Let’s be brutally honest. You have a folder full of saved bookmarks. “Master ChatGPT,” “Claude Secrets,” “Cursor for Beginners.” You read them, feel a brief rush of productivity, and then go back to doing your job exactly the way you did it three years ago.
Your work hasn’t fundamentally changed. You just tacked “let AI do it” onto your old habits. This is tutorial hell.
Hoarding AI tutorials isn’t ambition; it’s productivity theater.
You open a chat window, type “make me a product strategy,” and hit enter. The AI spits out a generic, 10-page hallucination. It has all the right buzzwords, but none of your actual business logic. So, you tweak the prompt. You rewrite it. You rage against the machine.
But the AI isn’t the problem. You are.
AI isn’t your omniscient employee; it’s a high-speed train with no tracks. If you don’t lay the tracks, it just crashes faster.
The real bottleneck in AI productivity isn’t the tool’s intelligence. It’s your failure to codify your own tacit knowledge. You expect AI to read your mind, yet you refuse to do the foundational work of explicitly defining your own processes.
Think about how you actually do your job. When you sit down to write a product requirement document, you don’t just start typing. In your head, you’re asking: Who is the user? What’s the scenario? What are the constraints? What happens if the system fails? This is your implicit logic. It’s the invisible scaffolding of your expertise.
And you’re keeping it entirely in your head.
When you ask AI to “write a PRD” without giving it this scaffolding, it has to guess. And it will guess poorly. We blame the model for hallucinating, but we never gave it the reality to anchor to.
The AI era isn’t about training the AI to be smarter. It’s about training ourselves to stop being so vague.
To actually leverage AI, you have to drag your implicit logic out into the daylight. You have to break your workflow into a rigid, repeatable assembly line. Call it a “Skill,” call it a workflow, call it whatever you want. The point is, you must write down: what happens first, what conditions must be met to move to step two, and what constitutes a “finished” product.
Stop asking AI to be a magical, end-to-end employee. It sucks at that. Instead, put AI into a production line. Let AI handle the “understanding and generating.” Let deterministic tools handle the math and formatting. Let humans handle the final judgment and acceptance.
If you’re making a monthly budget report, don’t ask AI to do the whole thing. Let AI organize the raw expense data. Let Excel do the math. Let yourself make the strategic decisions on where to cut costs.
The guilt of being stuck in tutorial hell ends when you stop chasing tools and start building systems.
Stop trying to find the perfect prompt. Start mapping your own brain.
FAQ
Q: Isn't AI supposed to be smart enough to figure out the context on its own?
A: No. LLMs are prediction engines, not mind readers. They predict the most statistically likely next word based on your input. If your input lacks business context, the output is statistically likely to be generic garbage. Intelligence without instructions is just noise.
Q: How do I actually start externalizing my tacit knowledge?
A: Start with your most repetitive task. Write down every single micro-decision you make in your head before you execute it. If you write an article, map out: topic selection -> research -> outline -> drafting -> fact-checking. Turn that into a checklist. Feed that checklist to the AI as its rules of engagement.
Q: If I write down every step of my job, won't AI just replace me?
A: If your job is just executing a rigid set of steps, AI was going to replace you anyway. By codifying your workflow, you elevate yourself from a factory worker to a factory manager. You become the one designing the system, not the one getting automated by it.