Problem before solution
A discovery process should turn a proposed solution back into the underlying problem before committing to build.
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A source-backed path for turning generated AI product ideas into problem framing, validation briefs, task-specific evals, and score-gated decisions.
A discovery process should turn a proposed solution back into the underlying problem before committing to build.
A validation brief compresses an idea into target user, problem, proof needed, and the smallest useful test.
A useful AI eval starts with a task-specific objective that names what the system must do well in its real product context.
An AI product should define how success will be evaluated before the team invests in deeper prompt, model, or workflow work.
Useful production logs can be converted into eval cases so real failures become repeatable tests.
Automated eval scores need human calibration so the measured result still matches the product question.
A scoring gate gives a generated idea a lightweight decision point before it receives more time.