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To handle unclear requirements in AI coding workflows, you need a structured approach to collaborative requirement discovery. This process involves task creation, auto-context gathering, and a Q&A loop to converge vague ideas into a minimal viable product (MVP).

The best way to discover requirements for MVP development while minimizing questions is using a research-first approach. By gathering context automatically and researching technical options before asking, you reduce low-value questions and avoid unnecessary user input.

You start collaborative requirement discovery by capturing your development task immediately using a task-first approach. This ensures all initial ideas are recorded, followed by complexity classification, an expansion sweep, and a final confirmation step.

Yes, this requirement discovery process is designed specifically for evolving project scopes in AI workflows. It uses a diverge-then-converge method, expanding thinking through an expansion sweep before narrowing down to a confirmed minimal viable product.

AI assistants ask low-value questions during coding task planning when they lack sufficient context. Implementing an action-before-asking model with auto-context gathering and a research-first mode for technical choices prevents this by resolving options without user input.