FATA Framework
First Ask Then Answer
Overview
A two-stage interaction paradigm where AI systems proactively generate supplementary questions before answering, rather than reactively clarifying only when ambiguity is detected.
The core move: use the model’s reasoning to scaffold user expression, helping non-experts provide the information that experts would know to ask for.
Components
Stage 1 — Systematic Question Generation: The system generates multi-dimensional questions covering contextual background, constraints, preferences, environmental factors, and historical context. All questions are produced at once (single-turn) rather than through iterative back-and-forth.
Stage 2 — Context-Enhanced Response: The system integrates original query with collected supplementary information to produce personalized, expert-level responses. The supplementary information creates intersecting constraints that narrow the solution space.
When to Use
- When users are non-experts in the problem domain
- When personalization significantly improves response quality
- When the cost of a wrong answer is high enough to justify the upfront investment
- When the interaction can tolerate a structured questioning phase
Limitations
- Adds friction to the interaction (questions before answers)
- Single-turn questioning may miss complex interdependencies
- Effectiveness depends on users actually answering the questions well
- Not appropriate for simple factual queries
- Quality of questions depends on the underlying model
Practical Considerations
The framework is prompt-only, no fine-tuning required. It can be integrated with existing tool-calling, RAG, or agent architectures as a preprocessing step.
Built-in constraints should prevent over-questioning and protect privacy. The questioning phase needs to feel like helpful scaffolding, not interrogation.
Related: 07-molecule—expert-consultation-externalization, 01-atom—information-scaffolding, 01-atom—expertise-information-gap