Best GitHub Copilot workflow for PR code reviews

Recommendation (short):
Use GitHub Copilot in your editors for fast, interactive review assistance (inline suggestions, quick review-comment drafts, and local test generation), and run a separate CI LLM job (ChatGPT or another controllable LLM API) to produce PR summaries, draft test candidates, and a curated set of suggested review comments. Keep a strict human-in-the-loop policy: generated suggestions must be reviewed and accepted by a human before merging.

Why this split works
- Copilot (editor) is best for reviewer ergonomics and rapid, context-aware suggestions while looking at code.
- A CI LLM job is auditable, can be pinned to a model version, and is easier to guard with rate limits, logs, and repo-level controls.

Editor config (recommended minimal VS Code settings)
- Enable inline suggestions for quick completions (github.copilot.enable = true; github.copilot.inlineSuggest.enable = true).
- Show acceptance telemetry off for sensitive repos and require explicit accept step.
- Configure file-scoped suggestions: limit Copilot to changed files/packages in your monorepo when possible (use workspace settings per package).

Guardrails (must-haves)
- Human review required: tag all AI comments with [AI-suggested] and require a second reviewer sign-off before merge.
- Limit context sent to any external LLM: in CI only send minimal diffs or focused file ranges, not entire repo.
- Pin model version and record prompts+responses in CI artifacts for audit and debugging.
- Automated tests always run on generated tests; mark generated tests so maintainers can find/trim flakies.
- Secrets & credentials: forbid sending secrets in prompts/inputs; run LLM jobs on private runners if policy requires.

Automation pattern (CI) — practical flow
1. Trigger: on pull_request or pull_request_target (use target to avoid leaking secrets).
2. Analyzer job: compute changed files and affected packages (path filters for monorepo).
3. LLM job: send only the relevant hunks + package.json/test entry to ChatGPT-like API with a concise prompt (examples below).
4. Output actions: (a) post a draft comment summarizing the PR + suggested reviewer checklist, (b) post a list of suggested review comments tied to file/line numbers, (c) optionally open a branch PR with generated unit tests for reviewer verification.
5. Human reviewer accepts and edits suggestions; CI gates (lint, unit/integration tests) must pass before merge.

Example prompt snippets (short)
- PR summary: "Summarize this PR in 3 short bullet points focusing on intent, risk, and test gaps. Files changed: [list]."
- Review comment: "For changed hunk X, suggest one concrete review comment addressing correctness, performance, or missing tests."
- Test generation: "Generate 2–3 unit tests for function foo in file X using Jest, include mocks for external calls and edge cases. Keep tests self-contained and fast."

Decision criteria (pick approach based on):
- Budget: CI LLMs cost tokens; prefer local Copilot+manual for tight budgets.
- Team size & review load: larger teams benefit more from CI summarization and automated test generation.
- Security & compliance: if strict, run LLM on-prem or avoid sending code outside org.
- Output quality: use CI LLM for consistent, auditable outputs; prefer Copilot for interactive refinement.

Best-for / Avoid-if
- Best-for: teams wanting faster review cadence, consistent PR summaries, and seed tests for reviewers to iterate.
- Avoid-if: extremely sensitive IP where external LLM use is forbidden or if you can’t dedicate time to review/triage AI outputs.

Practical checklist
- [ ] Enable Copilot inline suggestions in reviewers' editors.
- [ ] Add GitHub Action: diff analyzer → LLM job → annotate PR (tag [AI-suggested]).
- [ ] Store prompts/outputs as CI artifacts and pin model version.
- [ ] Require human approval for any AI-generated code/comments.
- [ ] Run full CI (lint/tests) on any AI-generated tests before merge.

If you want, I can draft the GitHub Action skeleton (jobs + prompt templates) that posts comments and optionally creates a branch with generated tests.

Compare GitHub Copilot and Cursor