3.3.2 - Name the Risks Yourself Before Users Find Them in Production

Launch risk

Have you named the biggest risks before users hit them?

The call

Name the launch risks before users find them. Otherwise the first real user session becomes the risk discovery process.

Why it matters

Naming risks before users find them means you can prepare a response instead of scrambling after the fact. AI can help surface edge cases quickly, but human judgement decides which risks are worth mitigating before launch and which can be handled if they appear. The difference is between controlled exposure and a surprise that erodes trust.

Explainer

A launch risk is not a vague worry. It is a specific thing that might break, what would trigger it and what response is ready. Until you can name one failure mode, one trigger and one response, launch risks stay invisible until users hit them. AI can help enumerate scenarios, but it cannot decide which ones matter.

Make the launch risk concrete

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

• Too vague: There are probably some edge cases we have not thought of.
• Concrete enough to test: If a content creator’s saved context contains only a few words, the AI search may return results no different from a generic search. The trigger is any context shorter than a sentence. The response is to prompt the user to add more detail before running the search.

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

Check the launch risk

• Pass: You can say what might break, what would trigger it and what response reduces the damage.
• Fail: If launch risk still means there might be some issues, it is not named well enough yet.

Do not launch 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/launch/launch-risks.md written and sharpened: the launch risk 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.