Writing PRDs For AI Features

Why this matters

Clear PRDs for AI features align product, data science, engineering, design, legal, and operations around one truth. In day-to-day work, you will define user outcomes, data needs, metrics, model boundaries, safety guardrails, UX flows, and a realistic release plan. A strong PRD prevents scope creep, reduces risk, and speeds up iteration.

• Real task: Frame a feature (e.g., AI reply suggestions) with success metrics and offline/online evaluation.
• Real task: Specify data sources, privacy constraints, and monitoring for drift and abuse.
• Real task: Align latency/cost budgets with UX and infrastructure limits.

Concept explained simply

A PRD (Product Requirements Document) for AI features is a single document that describes the user problem, desired outcome, data and model needs, quality metrics, safety considerations, and the rollout plan. Unlike regular features, AI features are probabilistic—so your PRD must define how "good enough" is measured and what happens when the model is wrong.

Mental model: Inputs → Model → Outputs → UX → Feedback loop

• Inputs: What data the system uses (user text, images, events, history).
• Model: How predictions are produced (model type, provider, constraints).
• Outputs: Formats and confidence; fallback behavior on uncertainty.
• UX: Where and how results appear; user controls, explanations, and undo.
• Feedback loop: Signals to improve quality (clicks, edits, rejections), with safe data handling.

Standard PRD structure for AI features

• Problem statement and goals
• Users and scenarios
• Non-goals and constraints
• Success metrics (product and model), guardrails, and thresholds
• Data sources, privacy, retention, and consent
• Model approach and quality standards (baseline vs. target)
• UX flows, prompts/inputs, and error/edge cases
• Latency, reliability, and cost budgets
• Evaluation plan: offline metrics and online experiments
• Safety and fairness checks; abuse prevention
• Release plan: milestones, phases, rollout strategy, and monitoring
• Open questions and risks

Copy-ready PRD outline (expand to use)

Title: [Feature name]

Problem & Goal: [User pain] → [Desired outcome]

Users & Scenarios: [Who, when, where]

Non-goals: [What we won’t do]

Success Metrics:

• Product: [Activation, task success, time saved, NPS]
• Model: [Precision/Recall, MRR, BLEU/ROUGE, toxicity rate, latency]
• Thresholds: [Launch if X; rollback if Y]

Data:

• Sources: [Logs, events, 1P/3P data]
• Privacy: [PII handling, consent, retention period]
• Bias/Fairness: [Segments to monitor]

Model & Inference:

• Approach: [Rule-based, classical ML, LLM, embeddings]
• Prompt/Features: [Inputs, templates]
• Constraints: [Context length, token usage, rate limits]
• Fallbacks: [Rules, cached results, human escalation]

UX:

• Entry points: [Where feature appears]
• Controls: [Edit, accept, reject, report]
• Messaging: [Confidence, disclaimers]

Perf/Cost:

• Latency budget: [P95 target]
• Uptime target: [SLO]
• Cost per request: [Budget]

Evaluation:

• Offline: [Dataset, split, metrics]
• Online: [A/B design, segments, duration]
• Quality review: [Human-in-the-loop criteria]

Safety:

• Content filters: [Toxicity, PII leakage, jailbreaks]
• Fairness checks: [Group-wise metrics]
• Abuse response: [Rate limits, blocks]

Release plan:

• Milestones: [Alpha, Beta, GA]
• Rollout: [Canary %, opt-in, guardrail thresholds]
• Monitoring: [Dashboards, alerts]

Open questions & Risks: [List and owners]

Worked examples

Example 1: Smart-Reply Suggestions for Email

• Goal: Reduce time to reply by 20% for short, routine emails.
• Users: Busy professionals on mobile.
• Non-goals: Full email drafting or long-form writing.
• Metrics:
  • Product: Reply time (median), feature usage rate, edit rate.
  • Model: Toxicity rate < 0.1%, suggestion acceptance rate > 25%.
  • Latency: P95 < 400 ms.
• Data: Recent email thread text (last 10 turns), no attachments; never store user content without consent; retention 30 days for aggregated metrics only.
• Model: LLM with prompt containing last messages + persona; profanity and PII filters applied; top-3 suggestions.
• UX: Suggestions appear above keyboard; tap to send or edit; long-press to report.
• Evaluation: Offline human rating set (helpfulness, tone) and online A/B measuring reply time and acceptance.
• Safety: Block medical/legal claims; rate limit to 5 calls/min/user.
• Release: 5% canary; roll forward if acceptance >= 25% and abuse reports <= 0.2%.

Example 2: Fraud Risk Scoring for Marketplace

• Goal: Reduce chargebacks by 15% with minimal false positives.
• Users: Trust & Safety analysts; automated rules engine.
• Non-goals: Manual case management tooling redesign.
• Metrics:
  • Product: Chargeback rate, analyst workload.
  • Model: Precision at high-risk threshold ≥ 0.8; Recall ≥ 0.6.
  • Latency: P95 < 200 ms for real-time checkout.
• Data: Device fingerprints, IP, account age, velocity features; strict PII handling, 90-day retention.
• Model: Gradient boosting or deep tabular model; calibrated probabilities; threshold tiers for action.
• UX: Scores shown with reason codes; actions: approve, review, block.
• Evaluation: Backtest on 6 months of labeled outcomes; online holdout test with analyst override.
• Safety/Fairness: Monitor by geography and payment method; appeals mechanism.
• Release: Shadow mode → limited auto-block at high scores → expand if precision holds.

Example 3: AI Search Relevance with Embeddings

• Goal: Increase search CTR by 10% and reduce zero-result queries by 30%.
• Users: Shoppers on web and app.
• Non-goals: Personalization in this phase.
• Metrics:
  • Product: CTR, add-to-cart rate, zero-result rate.
  • Model: NDCG@10 target +0.08 vs baseline; latency P95 < 300 ms.
• Data: Query logs, item titles/descriptions; periodic offline index refresh.
• Model: Dual-encoder embeddings + ANN retrieval; hybrid with keyword fallback.
• UX: Results with highlighted matched concepts; Did-you-mean and filters.
• Evaluation: Offline: NDCG on labeled pairs; Online: AA test to validate logging, then A/B for CTR.
• Safety: Block unsafe queries; ensure brand-safe content filters.
• Release: Country-by-country rollout with cost monitoring per query.

Step-by-step: Write your PRD draft today

1. Write the problem in one sentence: user, context, desired change.
2. Define success metrics: one product metric and one model metric with thresholds.
3. List data sources and privacy rules: what you will and won’t store.
4. Sketch the UX: where output appears, user controls, and error states.
5. Set budgets: latency P95, cost per request, uptime target.
6. Choose evaluation: offline metrics + minimal viable A/B plan.
7. Plan release: shadow → opt-in beta → canary % → GA, with rollback triggers.

Mini templates for metrics

• Launch if: Acceptance rate ≥ X% and Toxicity ≤ Y%.
• Rollback if: Complaint rate > Z% for 24h or latency exceeds budget for 2 hours.
• Success by week 4: +A% on product metric vs. baseline.

Data, metrics, and evaluation specifics

• Offline: Use a fixed dataset, document splits, and report variance across critical segments.
• Online: Predefine sample size and duration to avoid peeking; specify primary metric.
• Human review: Define rubric (helpfulness, safety, bias) and inter-rater agreement.
• Monitoring: Track inputs, outputs, cost, latency, drift, and incident reports.

Common mistakes and how to self-check

• Vague outcomes → Fix: Add measurable thresholds and rollback conditions.
• Ignoring cost/latency → Fix: Set budgets early; include caching/fallbacks.
• No safety plan → Fix: Include filters, rate limits, reporting, and appeals process.
• Data ambiguity → Fix: Specify sources, consent, retention, and access.
• One-time evaluation → Fix: Add ongoing monitoring and retraining cadence.

Self-check checklist

• Problem and users clear in one sentence.
• At least one product metric and one model metric with targets.
• Data privacy and retention rules stated.
• Fallbacks and error handling defined.
• Rollout and rollback criteria explicit.

Exercises

These mirror the exercises below. Do them in your own doc, then compare with the solutions.

1. Exercise 1: Draft a one-page PRD for AI reply suggestions in a customer support chat.
2. Exercise 2: Write the evaluation plan for an AI categorization feature (offline + online).

Practical projects

• Turn one of the worked examples into a PRD in your context (your product, your users).
• Create a risk register: top 5 risks, impact, likelihood, and mitigations.
• Design a monitoring dashboard spec: metrics, segments, alert thresholds.

Mini challenge

In 5 bullet points, specify the launch and rollback criteria for an AI summarization feature in a docs app. Include latency, cost, and safety thresholds.

Who this is for

• AI PMs, product managers, and tech leads shipping AI-enhanced features.
• Data scientists and engineers who need crisp product requirements.

Prerequisites

• Basic understanding of metrics and experimentation.
• Familiarity with user research and product discovery.
• High-level knowledge of ML/LLM capabilities and limits.

Learning path

• Start: Problem framing and user outcomes.
• Then: Data readiness, safety, and evaluation fundamentals.
• Next: Write PRDs and run small pilots.
• Finally: Operationalize monitoring and iteration.

Next steps

• Complete the exercises below and compare with provided solutions.
• Take the quick test to validate understanding. The quick test is available to everyone; only logged-in users get saved progress.
• Apply this to one real feature in your product within the next week.