AI Code Review: 7 Best Practices for Engineering Teams

AI code review tools are everywhere now. But most teams use them wrong — they install, get flooded with noise, and either ignore the AI or turn it off. The difference between teams that get massive value and those that don't comes down to how they configure and use AI reviews.

Here are 7 practices we've learned from working with engineering teams using AI-powered code review.

1. Use Risk Scoring to Prioritize Human Attention

Not every PR needs the same level of scrutiny. A typo fix in a README and a payment flow refactor shouldn't get the same review process.

AI risk scoring assigns each PR a level — Low, Medium, High, or Critical — based on:

• Change scope: How many files changed? How many lines?
• Sensitivity: Does it touch auth, payments, database schemas, or security-critical code?
• Complexity: Are there complex conditionals, race conditions, or state mutations?
• Dependencies: Are external APIs or third-party packages involved?

2. Write Custom Prompts for Your Domain

Generic AI reviews are useful but limited. The real value comes when you teach the AI about your codebase, your conventions, and your business rules.

Instead of relying on default prompts, configure AI to understand:

// Example custom prompt for a fintech codebase:
"Focus on:
- All monetary calculations must use BigDecimal, never float/double
- Database transactions are required for any balance mutation
- PII fields must be encrypted at rest
- Rate limiting is required on all public API endpoints
- Audit logging for any admin action"

The more specific your prompts, the more relevant the AI's feedback becomes. A generic "this could be improved" turns into "this monetary calculation uses float arithmetic which can cause rounding errors in financial transactions."

3. Don't Treat AI Comments as Gospel

AI reviews are a second pair of eyes, not a senior architect. They catch things humans miss (like SQL injection patterns at 6 PM on a Friday), but they also produce false positives.

The sweet spot: take security and risk warnings seriously. Take style suggestions as optional. Over time, tune your prompts to reduce the noise.

4. Enforce Conventions Separately from Reviews

Mixing style feedback with logic feedback creates noise. A comment about a missing semicolon next to a comment about a race condition makes both less impactful.

Best practice: use two separate AI passes:

1. Convention check (Check Run): Branch naming, commit messages, coding standards. Binary pass/fail. Runs as a GitHub Check Run that blocks merge.
2. Code review (PR Comment): Logic, security, architecture, performance. Nuanced feedback with risk scoring.

This way, developers fix conventions before review even starts. The actual code review focuses on what matters.

5. Use One-Click Fix for Mechanical Issues

When AI identifies a clear, mechanical fix — like replacing string interpolation with parameterized queries, adding a missing null check, or fixing an import — one-click fix eliminates the round-trip.

Without one-click fix:

1. AI comments on issue
2. Developer reads comment
3. Developer opens IDE
4. Developer makes fix
5. Developer commits and pushes
6. New review cycle triggers

With one-click fix:

1. AI comments with fix suggestion
2. Developer clicks "Apply Fix"
3. Done

This works best for security fixes, import corrections, type annotations, and convention violations. Don't use it for architectural changes — those need human judgment.

6. Configure Per-Repository, Not Just Per-Account

Your frontend React app and your backend Go API have different needs. A monorepo with multiple services needs different rules for each path.

Good teams configure AI reviews at the repository level:

• Frontend repos: Focus on accessibility, bundle size impact, component reuse patterns
• Backend repos: Focus on SQL safety, error handling, rate limiting, input validation
• Infrastructure repos: Focus on security groups, IAM policies, resource limits
• Mobile repos: Focus on memory leaks, battery usage, offline handling

One-size-fits-all AI prompts produce one-size-fits-all noise.

7. Measure and Iterate

Track these metrics to know if AI code review is actually helping:

• Review cycle time: How long from PR open to merge? (Should decrease)
• Bugs caught in review vs production: Are more issues caught pre-merge? (Should increase)
• Fix application rate: What percentage of AI suggestions do developers apply? (If below 20%, your prompts need tuning)
• Developer satisfaction: Do developers find AI reviews helpful or annoying? (Survey quarterly)

If your fix application rate is low, the AI is producing noise. Tune your prompts. If it's high but review time hasn't decreased, your process needs restructuring — perhaps you're requiring too many human approvals on low-risk PRs.

The Bottom Line

AI code review isn't magic. It's a tool — and like any tool, it works as well as you configure it. Teams that invest an afternoon in custom prompts, convention rules, and risk-based processes save dozens of developer hours per week.

The goal isn't to replace human code review. It's to make human reviewers focus on the things only humans can evaluate: architecture decisions, business logic correctness, and system design. Let AI handle the rest.