2.1.1 - The Only Path Your Prototype Actually Needs to Walk

Prototype path

Is this the only prototype path users need?

The call

Build one path first. Otherwise AI helps you prototype everything at once and you learn nothing clearly.

Why it matters

The only path your prototype needs should stay explicit so users can understand what the prototype is proving. AI can generate many possibilities quickly, but human judgement decides which path protects the intended learning outcome. The difference is between focused evidence and scattered feedback from a drifting prototype.

Explainer

A prototype path is not a mini product. It is the thinnest route that can answer the question you actually have. Until you can name one learning goal, one path to test and one signal that would prove it, the prototype will keep growing. AI can help build quickly, but it cannot protect a prototype from drift.

Make the prototype path concrete

Compare the broad version with a version you can actually test.

• Too vague: The prototype should show the main search features.
• Concrete enough to test: The prototype only needs to show whether a content creator can enter a question, see results shaped by their saved context and act on one result without needing to understand how the context works.

The second version lets two people build the same thin test from it.

Check the prototype path

• Pass: You can say what the prototype is meant to teach, which path will test it and what signal counts as proof.
• Fail: If the prototype still sounds like a lighter version of the full product, the path is not tight enough yet.

Do not move into prototype build or polish work until this passes.

What you'll walk away with

This post is about the framing decision: the words that pin down what this idea actually means for your build, before any code. You'll come out with your own knowledge-base/build/prototype-path.md written and sharpened: the prototype path pinned down as a decision, three worked examples to map against your own surface and an AI prompt that pressure-tests it until two people would make the same call.

The code that brings these decisions to life lives in the build-in-public repos (subCancel, ghostMarketingFlow and flowRun), which are works in progress growing alongside the writing. We work through the code together each week in the free weekly workshops; that is where these ideas get put into practice with hands on the keyboard.

If you sign up, this idea continues with how it all fits together, a worked example, how to use it with AI, how to evaluate it on a real change, the risks worth naming and how to mitigate them, the key takeaways and a copy-paste AI prompt you can drop straight into your next chat. Examples are shown on the Cloudflare Workers stack with AI-assisted coding tools; the ideas apply equally on any other platform.