AI Coding Rules Rollout Playbook for Engineering Teams

Why most rollouts fail

Most teams over-index on the document and under-invest in adoption mechanics. A long rules file is not a process. If developers don’t know when rules apply, where they live, and how exceptions are handled, compliance drops in week two.

Phase 1: Define one source of truth

Pick one source file per assistant and keep ownership clear:

• Cursor: `.cursorrules`
• Claude Code: `CLAUDE.md`
• Copilot: `.github/copilot-instructions.md`

Store policy rationale next to the rule, not in a separate wiki. Engineers should understand why a constraint exists.

Phase 2: Start with high-leverage rules only

Start with 8-15 rules max. Good starter categories:

• API contracts and typing discipline
• Error handling conventions
• Test expectations for changed code
• Security defaults (no hardcoded secrets, input validation)

Avoid style-only bikeshedding in v1. Teams adopt guardrails that prevent incidents, not preference debates.

Phase 3: Add enforcement that helps, not blocks

Use CI to check predictable constraints: lint, typecheck, test coverage floors on touched files. Keep AI rules aligned with CI checks so engineers see one system, not conflicting signals.

Phase 4: Measure behavior

Track:

• PR rework rate after AI-generated commits
• Security/static analysis findings per PR
• Time-to-merge by team

If a rule does not improve one of these metrics, rewrite or remove it.

Practical rollout cadence

Run a 30-day rollout:

• Week 1: baseline and pilot team
• Week 2: ship v1 standards
• Week 3: collect friction and tune
• Week 4: org-wide template + training snippets

This is enough to establish habit without waiting for a perfect framework.