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To generate a PRD for AI-enabled coding projects, you can automate collaborative requirements discovery by seeding an initial document and iteratively updating it as new knowledge is gathered. This structures the early planning phase and ensures a concrete starting point for any initiative.

Scoping an MVP across multiple implementation paths requires a diverge to converge planning approach. This method expands potential solutions through research-driven decision making and then converges to define the exact MVP scope, recording outcomes via ADR-lite.

Structuring task creation and requirements discovery involves applying a Step 0 task-first approach. This ensures a concrete starting point by automating the creation of a task and seeding an initial PRD before guiding research-driven decisions.

Automated requirements discovery supports team-based MVP scoping across codebases by applying structured, research-first decision making. It replaces manual, error-prone planning phases with an iterative PRD evolution process tailored for multiple collaborators.

Research-first decision making in product requirements planning is needed when facing unclear requirements or multiple implementation paths. It prescribes a structured approach to selecting options with trade-offs, ensuring research-driven decisions are recorded as ADR-lite outcomes.

Manual requirements discovery slows down AI coding projects because it is often error-prone and lacks structured iteration. Automating this phase ensures a task exists, seeds a PRD, and guides research-driven decision making to accelerate early planning.