Discovery Habit
What is a discovery habit?
Teresa Torres uses the term discovery habit in her book Continuous Discovery Habits to describe a recurring practice that gives a product team a clear feedback loop on the decisions they make about what to build. Defining clear outcomes, customer interviewing, story mapping, and assumption testing are all discovery habits.
The word "habit" matters. These aren't one-time activities you do at the start of a project. They are practices you repeat, week after week, so you always have recent feedback when you need to make a product decision.
What feedback does each discovery habit give you?
Each habit answers a different question about the decisions you are making:
- Defining clear outcomes. When you measure the impact of your releases against those outcomes, you get feedback on whether you built the right thing.
- Customer interviewing. When you learn about customers' unmet needs, pain points, and desires, you get feedback on whether you are solving the right customer problems.
- Story mapping and assumption testing. When you map your solutions and test the assumptions they depend on, you get feedback on whether your solutions will satisfy those customer needs.
Why are AI evals a new discovery habit?
When you build an AI product or workflow, there's one more decision to get feedback on: is the AI output any good? Error analysis of customer traces, designing evals, and running experiments to reduce errors give you that feedback loop. That's why Teresa calls evals a new discovery habit.
The habits also feed each other. If your error analysis is informed by what you learn from customer interviews and assumption testing, you can build confidence that your AI product will work better for your customers.
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Last Updated: September 1, 2026