3.1.2 - Ask for Signals, Not Opinions, From the People You Trust

Decision signal

Are you seeing real signals or just opinions?

The call

Ask for behaviour, not applause. Otherwise feedback confirms what you already believe while real friction stays hidden.

Why it matters

Asking for signals instead of opinions means feedback can improve decisions instead of just validating them. AI can organise patterns quickly, but human judgement decides which behaviour signal matters for launch. The difference is between noisy feedback and clear action tied to user outcomes.

Explainer

A decision signal is not just more feedback. It is an observable behaviour or result that can change the next product decision. Until you can name one observable signal, one behaviour behind it and one decision it will change, the conversation will drift into opinion. AI can help summarise reactions, but it cannot turn vague feedback into evidence on its own.

Make the decision signal concrete

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

• Too vague: We want feedback on whether people like the search results.
• Concrete enough to test: We want to see whether a content creator acts on a context-shaped search result in the same session, because that behaviour will tell us whether the context layer is adding real value or just adding steps.

The second version lets two people interpret the same evidence from it.

Check the decision signal

• Pass: You can point to the behaviour or result you need to see and explain what decision it will change.
• Fail: If you are still asking for thoughts, opinions or reactions in general, the signal is not defined well enough yet.

Do not move into feedback collection or iteration 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/launch/decision-signal.md written and sharpened: the decision signal 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.