Classic product requirements documents (PRDs) assume determinism: given the same inputs, the system behaves the same way. LLM features break that assumption. Outputs vary, costs vary, and failure modes can be subtle.
A good PRD for an LLM feature is less about detailed UI copy and more about constraints, evidence, and operating expectations.
Start with intent scope, not features
Define the set of supported intents as a list. Make out-of-scope intents explicit. This reduces ambiguity for users and for evaluation.
Include an escalation story for out-of-scope requests: a safe fallback response, a human handoff, or a structured search result (see human-in-the-loop and user transparency).
Write constraints as first-class requirements
LLM PRDs should include constraints that shape the system design:
- Data boundary. What data can enter prompts and tools, and what must be redacted or blocked (see data classification).
- Evidence requirements. Whether answers need citations, source links, or structured outputs (see citations and grounding and structured outputs).
- Cost and latency budgets. What is acceptable for the workflow (see SLO playbooks and latency engineering).
- Automation level. Read-only, draft-only, or action-taking via tools (see safe tooling).
Define your quality plan up front
PRDs should include how quality will be measured:
- Offline evaluation. A small test set and rubrics for correctness, groundedness and policy adherence (see evaluation rubrics).
- Regression suite. A set of cases that must not get worse when prompts or models change (see prompt regression testing).
- Online outcomes. Task completion, time saved, or deflection, with guardrails for risk (see value metrics and experimentation).
Specify telemetry and support needs
Supportability is part of requirements. Include the minimum telemetry needed to diagnose issues: prompt version, model route, retrieval sources, tool outcomes, and reason codes (see telemetry schema and decision logging).
Plan the rollout like a safety system
PRDs should include a rollout plan that is reversible:
- Feature flags and rings by user/tenant (see feature flags).
- Canary rollouts and auto-rollback triggers (see canary rollouts).
- Approvals for higher-risk changes (see approvals).
- Release notes and user messaging for behaviour changes (see release notes).
A compact definition of done
Before launch, ensure you have:
- Clear intent scope and out-of-scope behaviour.
- Data boundary rules and retention posture.
- Offline evaluation and a regression suite.
- Telemetry that supports debugging and audits.
- A reversible rollout plan and a support playbook.
A strong PRD does not eliminate uncertainty. It makes uncertainty manageable.