GenAI-assisted development playbook

Goals

• Reduce cycle time for repeatable engineering work such as implementation, refactoring, and scaffolding.
• Keep quality stable or improving through explicit checks and review discipline.
• Make adoption repeatable across a team instead of relying on a single expert user.

Where GenAI helps most

• Scaffolding and repetitive patterns such as DTOs, mappers, boilerplate, and standardized endpoints.
• Test creation for established patterns when acceptance criteria are clear.
• Refactoring under strong constraints.
• Documentation-first workflows including specs, checklists, and runbooks.

Guardrails

• Human ownership of design decisions and final code.
• Strict task decomposition into small, verifiable increments.
• PR size limits and single intent per PR.
• Mandatory self-review checklist.
• Explicit parity and regression checks for migrated or rewritten behavior.
• Stop conditions that reset scope or context when output quality degrades.

Recommended workflow

1. Brief: context, acceptance criteria, and constraints.
2. Plan: implementation steps, affected files, and test plan.
3. Implement: small increments with checkpoints.
4. Self-review: validate against checklist before handoff.
5. Tests and parity checks.
6. PR packaging: description, risks, and validation evidence.
7. Review iteration: targeted fixes with minimal scope changes.

PR readiness checklist

• Clear description of what changed, why it changed, and how to validate it.
• Evidence of tests executed and parity checks when applicable.
• Explicit risk callouts for behavior changes and edge cases.
• Clean scope with no unrelated refactors.
• Performance and security considerations addressed.

Common failure modes and mitigations

• Hallucinated APIs or wrong assumptions: require source-of-truth references and compile or test validation.
• Over-generation and scope creep: enforce PR caps, smaller tasks, and explicit out-of-scope notes.
• Subtle logic drift in migrations: use parity checklists and targeted regression suites.
• Over-reliance by mid-level contributors: reinforce training, templates, and stricter review gates.