1.1.1 - If You Can't Name the User, You're Guessing What They Want

Outcome

By the time you have worked through this idea you will have a user statement saved as knowledge-base/user.md: three lines for who the user is, what triggers their need and what outcome they are reaching for. Every later decision flows from those three lines. Scope, priorities, success criteria, what to leave out and the prompts you write to AI all inherit them. If the user stays vague, every decision gets softer. AI generates plausible options for an abstract audience instead of helping solve one real problem for one real person.

User definition

Can we name the user clearly before we decide what to build?

The call

Name the user first. Otherwise AI speeds up guesswork instead of solving a real problem.

Explainer

A user definition is not a market segment. It is one real user, the trigger that starts their need and the outcome they are trying to reach. Until you can state all three in plain language, the brief is too soft. AI can generate options, but it cannot choose the right boundary without a clear user.

Make the user definition concrete

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

• Too vague: This is for people who want to search the web with AI.
• Concrete enough to test: This is for a content creator who manages a website and needs to find gaps in what they have published so they can decide what to write next.

The second version lets two people make the same product decision from it.

Check the user definition

• Pass: You can say who the user is, what starts the need and what result they are trying to reach.
• Fail: The statement still sounds like a broad audience or a generic need.

Do not move into feature, scope or prototype work until this passes.

How it fits together

This is how the work is done in practice on the Cloudflare Workers stack with AI-assisted coding tools. The thoughts and ideas apply equally on any other platform.

The project is a monorepo so the user definition (alongside the rest of the framework files) lives in one shared knowledge-base/ folder that every app, every package and every AI prompt reads from. The three products in the vibe2value build-in-public stack (subCancel, ghostMarketingFlow and flowRun) each carry this layout, so look at any of them to see the structure in practice.

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.