PRD Template for AI/ML Products
AI/ML PRDs must specify model performance thresholds, training data requirements, evaluation metrics, latency budgets, and responsible AI guardrails.
This template adds ML-specific sections to the standard PRD for teams building AI-powered features.
Template sections
5 sections covering the complete prd workflow.
Model Requirements
Specify what the model must do in measurable terms: classification accuracy, latency (inference under 200ms p95), throughput, and model size constraints.
Data Specification
Document training data: source, volume, labeling methodology, refresh cadence, data quality criteria, and privacy considerations.
Evaluation Framework
Define offline metrics (precision, recall, F1), online metrics (engagement, task completion), and A/B test design with promotion/rollback criteria.
Failure Modes and Guardrails
Document expected failure modes: hallucination, bias, distribution drift, adversarial inputs. Specify guardrails for each.
Responsible AI
Address fairness testing, interpretability, escalation paths for harmful outputs, and accountability for model behavior in production.
Copy-paste template
# [Feature] PRD — AI/ML ## Model Requirements - Task: [Classification / Generation] - Accuracy: [X]% - Latency: [<200ms p95] ## Data - Source: [Description] - Volume: [X samples] - PII: [Yes/No] ## Failure Modes | Failure | Guardrail | Fallback | |---|---|---| | [Hallucination] | [Confidence threshold] | [Disclaimer] |
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