AI-Feature PRD Template: Turn a Generative-AI Idea into Contractor‑Ready Specs in 60 Minutes

Start with a single-sheet PRD containing seven fields: Problem & Job, User Context (with an example), Outcome Metrics, Prompt (or Model Strategy), Safety & Guardrails, Inference Cost Estimate, and Acceptance Tests (Given/When/Then + example data). This is a spec-first approach: the PRD should be copy-paste ready for contractors and QA, and should include the JSON-LD block for documentation or CMS publishing.

The goal is to remove ambiguity: name the exact user scenario, show a concrete input, define the expected output shape (JSON schema or sample), quantify success, and list explicit failure modes and handling. If you document these seven items tightly, engineering can estimate, designers can mock, and contractors can implement without multiple clarification loops.

Treat prompt design as a technical spec: include the exact prompt template, variables with types and allowed values, expected token budget, temperature/top-p settings, and a short note on whether fine-tuning or retrieval-augmented generation (RAG) is required. Give the contractor a golden example (input → prompt → expected output) and a rule for versioning prompts.

Where possible, make outputs machine-parseable: mandate output formats (JSON with a schema or JSON-LD snippet) and include a small parser test in the acceptance criteria. Prompt examples and structured outputs reduce back-and-forth and make automated testing tractable.

Document safety requirements explicitly: prohibited content categories, allowed transformations, required disclaimers, and escalation paths. For each forbidden output type, define the deterministic fallback behavior (deny with canned message, return reduced-scope output, or escalate to human review). These are product rules, not vague safety hopes.

Include observability instructions: which signals to log (input hash, prompt version, model response, confidence signals), what alerts to raise (rate of blocked responses, hallucination reports), and a review cadence. This keeps safety measurable and actionable for contractors and operators.

Include a concise cost model: expected calls/day, average input tokens, average output tokens, and model billing rate (or GPU hourly and throughput if self-hosted). Provide both per-request and monthly forecasts and a worst-case scenario with 3x unexpected traffic. This gives PMs and founders an immediate sense of run-rate risk before approving build.

Use simple formulas so contractors can validate: cost_per_request = (input_tokens + output_tokens) * price_per_token OR cost_per_request = hourly_infra_cost / requests_per_hour for hosted GPUs. Call out assumptions explicitly (session length, caching, rate limits) and add a note about where to plug real billing numbers once chosen.

Combine automated metrics with human checks. Specify task-appropriate automatic scores (exact-match for structured outputs, BERTScore/MoverScore or custom embedding-similarity for open text) plus a small human-evaluation plan: sample size, rubric, and pass thresholds. Automatic metrics help CI; human checks catch subtle problems like factuality and helpfulness.

Write acceptance tests as Given/When/Then scenarios that use the exact example data from the User Context field. Include parser-based assertions (JSON fields present, types match), metric thresholds (e.g., embedding similarity ≥ 0.8), and a human QA sign-off step. This yields contractor-ready tests and aligns expectations across teams.