# AI Product Sprint Checklist

Use this when a small team needs to move from product ambiguity to a verified shipped slice with AI support.

## 1. Frame The Sprint

- [ ] Write the user problem in one sentence.
- [ ] Identify the user, buyer, or operator affected by the work.
- [ ] Name the current workaround or pain.
- [ ] Define the desired behavioral outcome.
- [ ] Define the smallest useful shipped version.
- [ ] List the non-goals for this sprint.
- [ ] Decide which decisions are human-owned and which tasks AI can accelerate.

## 2. Gather Context

- [ ] Collect product docs, tickets, analytics, customer notes, and screenshots.
- [ ] Ask AI to summarize the evidence, but verify every claim against the source.
- [ ] Ask AI for missing questions, edge cases, and assumptions.
- [ ] Identify the current system constraints: data, permissions, API limits, design system, dependencies.
- [ ] Capture examples from competitors or adjacent products.
- [ ] Create a short brief with problem, audience, outcome, scope, and constraints.

## 3. Explore Solutions

- [ ] Generate 3-5 possible approaches.
- [ ] Score each approach by user value, implementation cost, risk, reversibility, and learning value.
- [ ] Select the most useful thin slice.
- [ ] Write the core user journey.
- [ ] Identify required states: empty, loading, success, error, partial, permission denied.
- [ ] Decide what can be mocked, stubbed, or hardcoded for the first pass.

## 4. Design The Experience

- [ ] Draft the screen hierarchy before visual polish.
- [ ] Check that the primary action is obvious.
- [ ] Write interface copy in plain user language.
- [ ] Define responsive behavior for mobile, tablet, and desktop.
- [ ] Confirm keyboard and screen-reader basics.
- [ ] Ask AI to critique the design for ambiguity, hidden assumptions, and missing states.
- [ ] Apply human judgment before accepting critique.

## 5. Build With AI Assistance

- [ ] Ask AI to inspect the existing code patterns before proposing code.
- [ ] Keep the first implementation close to existing architecture.
- [ ] Make small commits or checkpoints.
- [ ] Review generated code for data leaks, auth mistakes, brittle state, and unnecessary abstractions.
- [ ] Add focused tests around the risky behavior.
- [ ] Keep a short note of prompts or agent decisions that changed the implementation.

## 6. Verify The Slice

- [ ] Run build, lint, and relevant tests.
- [ ] Exercise the happy path manually.
- [ ] Exercise error and empty states manually.
- [ ] Check mobile and desktop layouts.
- [ ] Check performance basics: large assets, blocking work, repeated network calls.
- [ ] Ask AI for a final bug-risk review, then verify the findings yourself.
- [ ] Document known limitations and next steps.

## 7. Ship And Learn

- [ ] Confirm analytics events or success measures.
- [ ] Prepare release notes or internal handoff notes.
- [ ] Watch the first real usage or QA session.
- [ ] Collect user feedback.
- [ ] Decide whether to iterate, expand, pause, or remove.