interview-framework

Interview preparation is structured using a 3-phase algorithm: Understand, Propose Approaches, and Confirm & Store, which systematically captures user intent and validates requirements through adaptive question flow.

Intent-based depth scaling dynamically adjusts interview question complexity based on captured user intent, allowing varied depth during the understanding phase to efficiently match the interview's scope to the user's actual requirements.

Interview outcomes are captured and persisted using a context accumulator pattern that stores confirmed results to a file, enabling continuous improvement and preserving data across research, planning, and design phases.

Yes, approach synthesis is automated during the Propose Approaches phase, which processes the captured intent from the initial understanding phase to generate and present validated approaches for user confirmation.

The framework includes completion signal detection to manage conversation efficiency, identifying when a user has provided sufficient information to conclude the interview phase without asking redundant or unnecessary follow-up questions.

The best way to prepare interview questions for product research is using an adaptive 3-phase framework that scales question depth by intent, detects completion signals, and stores outcomes for continuous validation improvement.