Team Code Review Practices

Build team code review standards with AI — review guidelines, feedback culture, PR templates, knowledge sharing, and the practices that make reviews faster and more valuable for everyone.

Premium Course Content

This lesson is part of a premium course. Upgrade to Pro to unlock all premium courses and content.

Access all premium courses
1000+ AI skill templates included
New content added weekly

← Back to course overview

🔄 Quick Recall: In the previous lesson, you learned to measure and reduce technical debt. Now you’ll build the team practices that make code review efficient, educational, and positive — because tools are only as effective as the culture that uses them.

Code review is a team sport. The best tools and checklists fail if reviews are slow, feedback is unhelpful, or the culture is adversarial. AI can handle the mechanical checks, but the human elements — how feedback is given, how disagreements are resolved, and how knowledge is shared — determine whether reviews make the team better or just slower.

Review Guidelines

AI prompt for team review standards:

Create code review guidelines for my development team. Team size: [NUMBER]. Experience mix: [SENIOR/MID/JUNIOR RATIO]. Tech stack: [LANGUAGES AND FRAMEWORKS]. Generate: (1) What reviewers should focus on — a prioritized checklist (correctness → security → performance → maintainability → style, with style being lowest priority since formatters handle it), (2) PR requirements for authors — description template, maximum PR size, testing expectations, (3) Review etiquette — how to give feedback (explain why, include examples, use suggestions not commands), how to receive feedback (don’t take it personally, ask for clarification), (4) Escalation process — what to do when reviewers disagree, (5) Turnaround expectations — maximum time before first review, maximum time for full review cycle. Frame these as team agreements, not rules imposed from above.

Review focus hierarchy:

Priority	Focus Area	Example Comment
1. Correctness	Does it do the right thing?	“This filter excludes active users — should it include them?”
2. Security	Could this be exploited?	“User input goes directly into the query — use parameterized queries”
3. Performance	Will this scale?	“This queries inside a loop — consider a single JOIN”
4. Maintainability	Can the next developer understand this?	“Consider extracting this 50-line block into a named function”
5. Style	Does it follow conventions?	Handled by formatters — shouldn’t appear in review

PR Templates

AI prompt for PR template:

Create a pull request template for my team. Include sections for: (1) Summary — what does this PR do and why (not how — the code shows how), (2) Type — bug fix, feature, refactoring, chore, (3) Changes — bulleted list of what was modified, (4) Testing — how was this tested (automated tests, manual testing, edge cases considered), (5) Screenshots — if UI changes, (6) Checklist — [x] tests pass, [x] no new warnings, [x] documentation updated if needed, [x] PR is under 400 lines. The template should take 2-3 minutes to fill out and save the reviewer 10-15 minutes of context-building.

✅ Quick Check: A PR has this description: “Fix bug.” That’s it. What’s missing? (Answer: Everything. The reviewer doesn’t know which bug, what caused it, how it was fixed, what the fix’s scope is, how it was tested, or what to look for during review. A good description: “Fix: users seeing stale profile data after email change. Root cause: the profile cache wasn’t invalidated when email updated. Fix: added cache invalidation call after email update in UserService.updateEmail(). Tests: added unit test for cache invalidation, manually tested email change flow. Risk: low — change is isolated to one function.” This takes 2 minutes to write and saves 15 minutes of reviewer investigation.)

AI prompt for review as learning:

Analyze this code review interaction and suggest how to make it more educational. Reviewer comment: [PASTE COMMENT]. Author response: [PASTE RESPONSE]. Improve the exchange to include: (1) the technical principle behind the suggestion (not just “do it this way”), (2) a link to relevant documentation or a blog post, (3) an acknowledgment of the author’s approach before suggesting an alternative (“Your approach works for the current case, but here’s why X might be better as the data grows…”), (4) a code example showing the suggested alternative. Transform review comments from directives (“use a map”) into learning moments (“Maps are better here because…”).

Review Efficiency Practices

AI prompt for review efficiency:

Suggest ways to make code reviews more efficient for my team. Current process: [DESCRIBE — average PR size, review time, number of review cycles]. Identify bottlenecks and suggest: (1) PR size guidelines — what’s the optimal size for your team and what to do when work naturally produces large PRs (stacked PRs, feature flags, etc.), (2) Self-review checklist — what the author should check before requesting review (catching their own issues first), (3) Review rotation — how to distribute reviews fairly and prevent bottlenecks, (4) AI pre-review — which checks should be automated so human reviewers focus on high-value feedback, (5) Async vs. sync — when to leave written comments vs. have a quick call.

Key Takeaways

Review comments should explain “why” not just “what” — “use a Map because it preserves insertion order and performs better” educates, while “use a Map” just instructs. Team norms should require reasoning with every suggestion
PR size is the strongest predictor of review speed: PRs under 400 lines get reviewed same-day, while 500+ line PRs sit for days because reviewers can’t find 90 contiguous minutes. Smaller PRs are the single most effective process improvement
The 2-comment rule prevents extended text-based disagreements: if a review discussion reaches 2 rounds without resolution, move to a synchronous conversation where tone and whiteboard sketches resolve issues in 5 minutes that text can’t in 5 comments
PR descriptions save more reviewer time than any tool: a 2-minute description (what, why, how tested, risk assessment) saves 15 minutes of reviewer investigation and context-building
Code review is the team’s most powerful knowledge-sharing mechanism — every review comment that explains a principle teaches the author something that prevents the same issue in every future PR

Up Next

In the final lesson, you’ll build your personalized implementation plan — applying AI code review and refactoring to your specific codebase and team.

Knowledge Check

1. A senior developer leaves a review comment: 'This is wrong. Use a map instead.' The junior developer doesn't understand why a map is better and feels criticized. What's the review culture problem?

The senior should be less blunt — add 'please' or a smiley The comment lacks explanation and framing. A better comment: 'Consider using a Map here instead of the object. Why: (1) Map preserves insertion order, which matters for this sorted display, (2) Map.has() is clearer than checking for undefined, (3) Map performs better for frequent additions/deletions. Here's how it would look: [code example]. What do you think?' This comment: explains the reasoning (the reviewer learns), provides a code example (makes the fix clear), and asks for input (respects the author's judgment). The cultural fix: establish a team norm that review comments explain 'why,' not just 'what' The junior needs thicker skin — code review is about the code, not feelings

2. Your team's average PR review takes 48 hours. Developers often context-switch 3 times before getting reviewed. What's the process improvement?

Set a 24-hour review SLA — enforce faster reviews Reduce the reason reviews take 48 hours by fixing the PR side: (1) Smaller PRs — a 100-line PR takes 15 minutes to review. A 500-line PR takes 90 minutes and gets delayed because no one has 90 contiguous minutes. Target: under 400 lines per PR. (2) AI pre-review — when AI handles mechanical issues first, the human review is faster and higher-quality, (3) PR description quality — a clear description of what changed and why reduces the reviewer's ramp-up time from 10 minutes to 2 minutes, (4) Assigned reviewers — 'someone should review this' means no one reviews it. Assign a specific reviewer. AI helps with all four: splitting large PRs, pre-reviewing for mechanical issues, generating PR summaries, and recommending the best reviewer based on file ownership Have more developers do reviews — distribute the load

3. Two developers disagree on an approach during code review. The discussion goes back and forth for 5 comments over 2 days. Neither is willing to compromise. What should happen?

The senior developer's opinion wins A decision framework that prevents prolonged debate: (1) 2-comment rule — if a review discussion reaches 2 rounds of disagreement, move it to a synchronous conversation (Slack huddle, quick call) — text is terrible for resolving disagreements. (2) Objective criteria — 'I prefer' is subjective and unresolvable. 'This approach is O(n²) where O(n) is possible' is objective. AI can help by analyzing both approaches objectively: performance implications, maintainability impact, and how each fits existing codebase patterns. (3) Escalation — if the synchronous conversation doesn't resolve it, bring in a third team member for a tiebreak, or defer to the team's established style guide. (4) Time-box — no single review discussion should block a PR for more than 24 hours Split the difference — find a middle ground between the two approaches

Answer all questions to check

Complete the quiz above first

Related Skills

Code Reviewer Pro Code Reviewer Pro