Code Review Best Practices for High-Velocity Engineering Teams

The best engineering teams treat code review as a force multiplier — not a gate. Done right, review catches bugs early, spreads knowledge, enforces standards, and builds shared ownership. Done wrong, it creates bottlenecks, causes burnout, and generates resentment. Here are the practices that separate the best from the rest.

1. Keep PRs small and focused

The single highest-leverage practice in code review is PR size control. Small PRs — ideally under 400 lines of diff — receive better review, merge faster, and cause fewer conflicts. Large PRs create cognitive overload for reviewers, who inevitably miss issues when overwhelmed by scope.

Aim for PRs that do one thing. If a feature requires both a database migration and an API change, consider splitting them. Merlin AI Code Review's /describe command helps authors articulate exactly what a PR does — and if the description requires five bullets, the PR is probably too large.

2. Separate automated from human review

Human attention is expensive and should be spent on things that require human judgment: architecture decisions, business logic correctness, UX implications, team conventions. Style issues, obvious bugs, missing documentation, and security anti-patterns should be caught automatically.

Merlin AI Code Review handles the automated layer — running the moment a PR opens, posting inline comments on mechanical issues, and leaving human reviewers to focus on the 20% that actually requires their expertise.

3. Provide actionable, not just critical, feedback

The best review comments explain the problem and suggest a solution. "This could cause a null pointer exception if user is undefined — add a null check on line 42" is dramatically more useful than "null pointer risk here". Merlin AI Code Review's AI review follows this pattern: every comment includes what the problem is, why it matters, and how to fix it — often as a GitHub suggestion block that's one click to apply.

4. Review the intent, not just the implementation

Strong reviewers ask: "Is this the right thing to build?" as well as "Is this built correctly?" AI review excels at the second question. Human reviewers should prioritize the first — does this PR solve the right problem, is it consistent with the architecture, will it cause unintended side effects elsewhere in the system?

5. Establish and automate your standards

Style debates in code review are a waste of everyone's time. Document your team's conventions, then enforce them automatically. Merlin AI Code Review's RAG pipeline indexes your codebase and learns your team's actual patterns — not generic style rules, but the specific idioms your team uses. Deviations from your actual conventions get flagged automatically.

6. Set response time expectations

Unclear expectations about review turnaround are a leading source of engineering friction. High-performing teams commit to a specific SLA — e.g., first response within 4 hours on business days. With Merlin AI Code Review providing immediate first-pass feedback, this SLA applies to human follow-up review, not initial feedback. Authors can iterate on AI feedback while waiting for human review.

7. Use review to teach, not just gatekeep

The most valuable code review comments help the author grow, not just fix the immediate issue. Explain why a pattern is problematic. Link to relevant documentation. Suggest alternative approaches. Merlin AI Code Review's comments include educational context by design — and the /ask command lets junior engineers get explanations without blocking a senior reviewer.

8. Close the loop on review feedback

When a reviewer flags an issue, the resolution should be visible. Did the author fix it, or did they push back with a good reason? Unresolved threads are a sign of review drift. Merlin AI Code Review's /approve command provides an AI-assisted verdict: it re-reviews the PR after changes and approves if blocking issues are resolved, giving reviewers a signal about whether to re-examine or rubber-stamp.