Code review is where many teams quietly lose time. A pull request waits for two days, the author moves on to something else and the eventual reviewer skims because the diff is 900 lines. By then even a small comment creates another round of context switching.
The following practices are the ones I find useful whether the first review is done by a person, an AI code reviewer, or both.
1. Keep pull requests small
This is usually the change with the biggest payoff. Review quality gets worse as diffs grow because people stop following every detail. Try to make a PR do one thing and keep it small enough that somebody can review it without reserving half of their afternoon.
Small PRs are easier to reason about, faster to approve, safer to revert, and far less likely to hide a bug in the noise. If a change is genuinely large, split it: scaffolding first, then behavior, then cleanup.
2. Write a description that explains why
A diff shows what changed. It can't show why. A good PR description gives the reviewer the intent, the approach, and what to look at carefully:
- What this change does, in one or two sentences.
- Why it's needed: the bug, the feature, the constraint.
- How to verify it, and anything you're unsure about.
A reviewer who understands intent catches design problems, and an AI reviewer with the same context produces far better feedback too.
3. Set a review SLA
The biggest source of wasted time in review is latency. A PR that waits a day forces the author to context-switch away and back. Agree on a team norm (for example, "open PRs get a first response within four working hours") and treat review as interrupt-priority work that blocks other engineers, not something you get to eventually.
You won't always hit it, but having the number changes behavior. To see where you stand today, the PR cycle time analyzer measures open-to-merge time for any public repository.
4. Review for the things that matter
Not all feedback is equal. Spend reviewer attention on, in rough priority order:
- Correctness: does it do what it claims? Edge cases, error handling, race conditions.
- Security: input validation, authorization, secrets, injection.
- Design: does it fit the architecture, or add debt?
- Readability: will the next person understand it?
- Style: naming, formatting, conventions.
Automate the bottom of that list so people can focus on the top. Formatting and simple style rules should be handled by tooling instead of reviewers leaving the same nit: comment for the hundredth time.
5. Separate blocking from non-blocking feedback
Ambiguous feedback stalls PRs. Make it obvious what must change versus what's a suggestion. A simple convention works well:
- Blocking: "This will crash on an empty list."
- Non-blocking (nit/suggestion): "Optional: this reads cleaner as a map."
When everything sounds equally urgent, authors either over-revise or get defensive.
6. Be kind and specific
Review comments are read by a person who wrote the code carefully. Critique the code instead of the author; explain the reasoning and offer a concrete alternative when you can. "Why this approach over something else?" supports a conversation. Good review culture is what makes people want to send small, frequent PRs.
7. Use AI as the first reviewer
An AI code reviewer reads every pull request the moment it opens and handles the mechanical first pass, within minutes. By the time a human looks, the author has already fixed the easy stuff.
That does two things: it shortens review latency (practice #3), and it frees your reviewers to spend their limited attention on correctness and design (practice #4) instead of catching the same class of mistakes by hand on every PR.
Keep it tuned for signal over volume. A reviewer, human or AI, that comments on everything trains people to ignore it. Configure your rules so the feedback is rare, accurate, and worth reading. If you run the reviewer in CI, the guide to AI code review with GitHub Actions covers permissions, forks and secret handling.
8. Make conventions executable
Every team accumulates rules: "always use the typed client", "never log request bodies", "new endpoints need a rate limit". If those rules live only in people's heads, they're enforced unevenly and lost when someone leaves. Write them down, and better, make them executable, either as lint rules or as custom rules your code review tool applies to every PR automatically.
Putting it together
Good review is not about asking people to concentrate harder. It mostly comes from small PRs, clear intent, a reasonable response time and tooling that handles the repetitive parts.
Scopy AI is an open-source AI code reviewer that handles the first pass, applies your custom rules and leaves inline comments on GitHub.
Next, learn how AI code review works under the hood, or how to keep your code private with self-hosted AI code review.