When joining an AI project I prioritize documentation that removes immediate blockers for contributors and clarifies intent, safety, and reproducibility. Top priorities: README (quick start + architecture overview), model card (purpose, data, metrics, limitations, risk), data schema & provenance, experiment reproducibility guide (env, seed, config, checkpoints), API/interface spec, and a short onboarding FAQ.
Two‑week practical checklist (owners and deliverables):
Week 0–1 (Day 1–5)
- Create/cleanup README — purpose, repo layout, quick start (conda/docker, sample inference): owner: me; deliverable: README.md
- Add high-level architecture diagram & component list: owner: me; deliverable: arch.png + short doc
- Capture data schema & provenance: fields, types, sample rows, access instructions: owner: data eng; deliverable: data_schema.md
- Add CONTRIBUTING + coding style and branch strategy: owner: me; deliverable: CONTRIBUTING.md
Week 1–2 (Day 6–14)
- Minimal model card draft: intended use, training data summary, metrics, known biases, mitigation: owner: me; deliverable: MODEL_CARD.md
- Experiment reproducibility guide: exact env (Dockerfile/requirements), training command, seed, config examples, where checkpoints are stored: owner: me; deliverable: REPRO.md + example run script
- Postmortem / known issues & FAQ: quick fixes, contact points: owner: team lead; deliverable: FAQ.md
- Add CI smoke test (basic training/serve pipeline) and link docs to PR template: owner: infra; deliverable: CI job + PR template
Why: These reduce onboarding time, prevent unsafe misuses, and make experiments reproducible. Each doc should be short, actionable, and linked from README so new hires can get productive within a day.
