Key Takeaway
AI feature design docs that include explicit evaluation criteria and rollout gates prevent the common failure mode of shipping a prototype directly to production.
When to Use This Template
Use this design doc template before starting development on any AI-powered feature. It covers the additional dimensions that AI features require beyond standard feature design: model selection rationale, evaluation methodology, gradual rollout with quality gates, and ongoing monitoring commitments. The template is designed to align engineering, product, and leadership stakeholders on both the opportunity and the operational commitment an AI feature entails.
Template Sections
Define the user problem this AI feature solves, the success metrics that will determine whether it is working, and explicit non-goals that bound the scope. Include: User need (who has this problem and how they experience it today), Success metrics (quantitative measures with target values and measurement method), Non-goals (what this feature explicitly will not do, to prevent scope creep), and Alternatives considered (why AI is the right approach vs. rules-based, manual, or no solution).
Document the technical implementation plan: Model selection (which model, why, and what alternatives were evaluated), Prompt design or training strategy (for LLM features: system prompt, few-shot examples, output format; for custom models: training data, architecture, hyperparameter approach), Data requirements (what data is needed, where it comes from, quality requirements), Integration architecture (how the AI component connects to the rest of the system), and Error handling (what happens when the model returns low-confidence, invalid, or harmful output).
Define how you will measure whether the AI feature meets quality requirements before and after launch: Offline evaluation (benchmark dataset, metric definitions, baseline to beat), Human evaluation (evaluation protocol, inter-rater reliability, sample size), Online evaluation (A/B test design, traffic allocation, statistical significance criteria, guardrail metrics), and Regression testing (automated test suite that runs on prompt or model changes to detect quality regressions).
Define the staged rollout from internal testing to full production: Internal dogfooding (team testing with real use cases), Limited beta (percentage rollout to subset of users with feedback collection), Gradual expansion (increasing rollout percentage with quality gates at each stage), Full launch (criteria for declaring the feature production-ready), and Rollback criteria (specific conditions that trigger an automatic or manual rollback).
Document the ongoing operational commitment: Quality monitoring (automated quality scoring, drift detection, feedback loop), Cost monitoring (per-request cost tracking, budget alerts, cost optimization plan), Incident response (on-call ownership, runbook, escalation path), and Maintenance schedule (model update cadence, prompt review frequency, evaluation set refresh).
Estimate the total cost of the AI feature: Inference cost (per-request cost multiplied by projected volume at 3, 6, and 12 months), Infrastructure cost (serving, caching, monitoring), Development cost (engineering time for build, evaluation, and launch), and Ongoing maintenance cost (engineering time for monitoring, model updates, evaluation set maintenance). Present as a monthly run-rate with assumptions clearly stated.
ai-feature-design-doc.md
MD · 8 KB
Complete AI feature design document template in Markdown format
The evaluation plan is the section most commonly skipped and most commonly regretted. Define your evaluation methodology before writing any code. It takes less than a day to create a benchmark dataset but weeks to debug quality issues that a benchmark would have caught.
Version History
1.0.0 · 2026-03-01
- • Initial AI feature design document template