# Spec-first AI feature delivery

> Turn one approved product requirement into a small, tested AI-assisted feature with a human-owned release decision.

- Canonical: https://gptnavi.com/workflows/spec-first-ai-feature-delivery
- Category: Coding
- Difficulty: Intermediate
- Setup time: 2 hours
- Estimated time saved: 4-10 hours
- Last materially updated: 2026-08-19
- Best for: Product engineers, Technical founders, Startup teams
- Tools: Kiro, Claude Code, Warp, Linear, PostHog

## Quick answer

A specification keeps coding agents useful. It makes the desired behavior, boundaries, test cases, and release owner clear before implementation starts.

## When to use it

- Small customer-facing feature
- Bug fix
- Internal workflow
- Prototype hardening

## Steps

1. **Write the behavior contract** — Define the user job, success state, acceptance criteria, non-goals, permissions, edge cases, and measurable release signal. Tool: Linear. Expected output: A bounded implementation ticket.
2. **Generate an implementation spec** — Turn the ticket into a file-level implementation plan, task list, risks, and test cases; correct it before code changes begin. Tool: Kiro. Expected output: An approved engineering spec.
3. **Implement in a reviewable branch** — Ask the coding agent to make only the approved changes, explain decisions, and run the relevant tests. Tool: Claude Code. Expected output: A focused pull request or branch.
4. **Reproduce and inspect** — Run the feature through its normal and failure paths from the terminal, then attach the test evidence to the ticket. Tool: Warp. Expected output: Reproducible test evidence.
5. **Release with one metric** — Deploy through the normal review path and watch the single customer outcome, error signal, or drop-off point that justified the work. Tool: PostHog. Expected output: A human-approved release decision.

## Prompt templates

### Feature spec

Turn this product request into a compact engineering spec. Include user outcome, acceptance criteria, non-goals, affected surfaces, permissions, edge cases, test cases, rollout metric, and rollback condition. Request: [paste]

### Change review

Review this implementation against the approved spec. Find missing acceptance criteria, regressions, unsafe assumptions, confusing UX, and missing tests. Give evidence and the smallest corrective action. Spec and diff: [paste]

## Common mistakes

- Letting an agent choose the product scope
- Treating an implementation plan as accepted requirements
- Measuring only deployment success instead of user behavior

## Related workflows

- https://gptnavi.com/workflows/agentic-engineering-delivery-loop
- https://gptnavi.com/workflows/llm-evaluation-before-production
- https://gptnavi.com/workflows/product-idea-to-coding-feature-spec