The vibe coding trap
Since AI went mainstream, vibe coding has become the default habit. You prompt, it builds, and the result looks like it works. The problem: most of the time, you don't really know what the agent just did for you.
Learning with AI tends to go the same way. While someone explains, everything makes sense — you nod along. But the moment you have to build it yourself from scratch, you're blank. You understood it while being taught; you freeze when practicing.
Both trace back to the same root: you become a consumer of answers, not a builder.
Start from a case study, not the docs
The key to this workflow is simple: start from a case study first, not the material.
Few things are more boring than reading documentation page by page with no direction. But the moment there's one real project you want to finish, every concept instantly has context: why it's needed, where it's used, and what breaks when you get it wrong.
The prompt looks roughly like this:
I want to learn AI agent orchestration using LangChain, with case study: AI code reviewer multi-agent (security, performance, code style).
One prompt, and the direction is set: what technology, what project.
Four steps, one phase at a time
Once the direction is clear, have the agent run four steps:
- Research — the technology, libraries, concepts, best practices, findings. The agent checks the current state: which version, which approach fits the goal, what's deprecated or plain tricky.
- Design architecture — project structure, data flow diagram, roadmap. You review and agree on it together before anything gets built.
- Phase breakdown — split the work into the smallest possible phases. Each phase should be small enough to finish in one sitting.
- Iterate — run one phase first, verify it works, then move on to the next.
The output is a step-by-step tutorial: everything you need to do, complete with code and explanations. Your job is simply to follow it.
The main rule: the agent is NOT allowed to write code
Now, the part that matters most: the agent is forbidden from writing code. You, the human, must type it yourself.
Why? Because typing the code manually surfaces questions and errors on almost every line — and that's exactly where the learning happens:
- "Why does it have to be this way?"
- "What does this line do?"
- "Why is this throwing?"
Those questions turn the session into an actual conversation. The agent explains, you push back, and sometimes you're right and there's a better way. Two-way interaction — not just accepting the output.
Memorize and understand, not just ship
It all comes down to two things: memorize and understand. Not "done building," but genuinely knowing why and how each part works.
This way you don't lose the essence of coding. You still write, read, and understand the code line by line — instead of having it all generated for you. As a bonus, your muscle memory for writing code stays intact.
This workflow is now packaged as a playbook you can point any agent at: the AI-Guided Learning Playbook. Point your agent to it, name the technology and the project you want to learn, and the rest runs itself.