PRD Template for AI/ML Products
AI/ML product PRDs require sections that traditional software PRDs do not cover: model performance requirements, training data specifications, bias mitigation strategies, and responsible AI considerations. This template adds ML-specific sections to the standard PRD.
Every AI feature must document its failure modes, fairness criteria, and human oversight mechanisms.
Template sections
6 sections covering the complete prd workflow.
Problem Statement
Describe the problem with ML context: what prediction or generation task is needed, what is the current approach (rules-based, manual, competitor solution), and what improvement is expected. Include the business metric that ML will optimize.
Model Requirements
Specify model performance targets: accuracy, precision, recall, F1 score, or other relevant metrics. Define the acceptable latency for inference. Document the fallback behavior when model confidence is below the threshold.
Data Requirements
Document training data needs: data sources, volume, labeling requirements, and data quality criteria. Specify data pipeline requirements: ETL frequency, feature engineering, and data versioning. Include data privacy requirements (anonymization, consent, retention).
Bias and Fairness
Document fairness criteria: which demographic attributes must be tested for bias (age, gender, race, geography). Specify acceptable disparity thresholds. Plan for regular bias audits and include mitigation strategies (re-sampling, re-weighting, adversarial debiasing).
Responsible AI
Document explainability requirements: can users understand why the model made a decision? Specify human-in-the-loop requirements for high-stakes decisions. Include kill switch and rollback plans if the model produces harmful outputs.
Success Metrics
ML-specific metrics: model accuracy in production (vs. test set), prediction latency p95, false positive/negative rates, user trust metrics (override rate, feedback rate), and business impact of the ML feature vs. the baseline.
Copy-paste template
# [Feature] PRD — AI/ML ## Problem Statement [ML problem with business metric context] ## Model Requirements - Task: [Classification / Regression / Generation] - Target metric: [Accuracy > X% / F1 > Y] - Inference latency: [< Xms p95] - Confidence threshold: [X%] - Below-threshold behavior: [Fallback] ## Data Requirements - Training data: [Source, volume, labels] - Pipeline: [ETL frequency] - Privacy: [Anonymization requirements] ## Bias and Fairness - Protected attributes tested: [List] - Disparity threshold: [< X%] - Audit frequency: [Quarterly] ## Responsible AI - Explainability: [Required / Nice-to-have] - Human oversight: [Required for high-stakes] - Kill switch: [Mechanism]
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