Writing PRDs
Scope
Covers
- Turning a product idea into a decision-ready PRD with unambiguous scope, requirements, and success metrics
- Optionally producing a PR/FAQ (press release + FAQ) to force customer-centric narrative first
- For AI features: adding a Prompt Set + Eval Spec so “requirements” are testable and continuously checkable
When to use
- “Write a PRD / product spec / requirements doc for this feature.”
- “Turn these messy notes into a PRD we can align on.”
- “Create a PR/FAQ and then a PRD.”
- “This is an AI feature; I need evals + prompts to define behavior.”
When NOT to use
- You’re still choosing *what strategy/market to pursue* (do product vision / strategy first)
- You need discovery from scratch (research plan, problem validation) more than requirements
- You need a detailed engineering design doc (APIs, schemas, low-level architecture)
- You’re prioritizing among many initiatives (do roadmap prioritization first)
Inputs
Minimum required
- Product + target user/customer segment
- Problem statement + why now (what changed, what’s broken, or what opportunity exists)
- Goal(s) + non-goal(s) + key constraints (timeline, policy/legal, platform, dependencies)
- Success metric(s) + 2–5 guardrails (quality, safety, cost, latency, trust)
If it’s an AI feature (additionally)
- What the model/system should do vs must never do (policy + safety)
- Concrete examples of desired and undesired outputs
- How correctness will be evaluated (offline tests, human review, online metrics)
Missing-info strategy
- Ask up to 5 questions from references/INTAKE.md.
- If answers are still missing, proceed with clearly labeled assumptions and provide 2–3 options (scope, metric, rollout).
Outputs (deliverables)
Produce a PRD Pack in Markdown (in-chat; or as files if the user requests):
- Context snapshot (what decision we’re making, constraints, stakeholders)
- Artifact selection (PR/FAQ vs PRD vs AI add-ons)
- PR/FAQ (optional) — customer narrative + FAQs
- PRD — goals/non-goals, requirements (R1…Rn), UX flows, metrics, rollout
- AI Prompt Set (if AI) — versioned prompts + examples + guardrails
- AI Eval Spec (if AI) — acceptance tests + judge prompts + pass/fail criteria
- Risks / Open questions / Next steps (always included)
Templates: references/TEMPLATES.md
Workflow (8 steps)
1) Decide the artifact set (don’t over-document)
- Inputs: User request + constraints.
- Actions: Choose: PR/FAQ only, PRD only, PR/FAQ → PRD, or PRD + AI add-ons (Prompt Set + Eval Spec).
- Outputs: Artifact selection + rationale.
- Checks: The artifacts match the decision being made and the audience.
2) Intake + clarify decision and success
- Inputs: references/INTAKE.md.
- Actions: Ask up to 5 questions; confirm decision owner, timeline, constraints, and success metrics/guardrails.
- Outputs: Context snapshot.
- Checks: You can state “what we’re deciding” and “how we’ll measure success” in 1–2 sentences.
3) Write the customer narrative first (PR/FAQ or PRD narrative)
- Inputs: Context snapshot.
- Actions: Draft a customer-centric narrative (problem → solution → why now). If using PR/FAQ, draft the press release headline/summary and top FAQs.
- Outputs: Narrative section (and PR/FAQ if selected).
- Checks: A stakeholder can restate the customer benefit and urgency without jargon.
4) Lock scope boundaries (goals, non-goals, out of scope)
- Inputs: Narrative + constraints.
- Actions: Define goals, non-goals, and explicit exclusions; call out dependencies and assumptions.
- Outputs: Scope section(s) in the PRD.
- Checks: “What we are NOT doing” is as clear as what we are doing.
5) Convert scope into testable requirements (R1…Rn)
- Inputs: Goals + user journeys.
- Actions: Write numbered requirements with acceptance criteria, edge cases, and non-functional needs (privacy, latency, reliability). Mark “must/should/could”.
- Outputs: Requirements table/list.
- Checks: An engineer or QA can turn requirements into test cases without asking you to interpret intent.
6) Define UX flows + instrumentation plan
- Inputs: Requirements + current product surfaces/events.
- Actions: Describe key user flows/states; specify success metrics, guardrails, and event/data needs (what to log, where, who owns).
- Outputs: UX/flows section + metrics & instrumentation section.
- Checks: Every goal has at least one measurable metric and a realistic data source.
7) If AI feature: ship prompts + evals as “living requirements”
- Inputs: Requirements + examples.
- Actions: Create a versioned Prompt Set and an Eval Spec (judge prompts + test set + pass thresholds). Include red-team/failure modes.
- Outputs: Prompt Set + Eval Spec drafts.
- Checks: The eval suite can fail when behavior regresses and pass when requirements are met.
8) Quality gate + finalize for circulation
- Inputs: Full draft pack.
- Actions: Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks/Open questions/Next steps.
- Outputs: Final PRD Pack (shareable as-is).
- Checks: Decisions, owners, metrics, and open questions are explicit.
Quality gate (required)
- Use references/CHECKLISTS.md and references/RUBRIC.md.
- Always include: Risks, Open questions, Next steps.
Examples
Example 1 (B2B SaaS feature): “Write a PR/FAQ + PRD for ‘Saved views’ in our analytics dashboard for admins.” Expected: PR/FAQ narrative, a scoped PRD with R1…Rn, metrics/guardrails, and a rollout plan.
Example 2 (AI feature): “Write a PRD + Prompt Set + Eval Spec for an ‘AI email reply’ assistant with brand tone constraints.” Expected: requirements that include safety/brand constraints, a prompt set with examples, and an eval spec with judge prompts + pass/fail thresholds.
Boundary example: “Write a PRD for ‘make onboarding better’ (no product context).” Response: ask the minimum intake questions; if context remains missing, produce 2–3 scoped options + assumptions and recommend discovery before committing to requirements.