AI Prompts for Code Review

Quick-Start (Copy This Right Now)

Are you using AI to review code the same way you'd use a linter? Because if you are, you're getting linter-quality feedback — and code review is supposed to be much more than that.

Here's a prompt you can use right now for any pull request:

You are a senior software engineer conducting a code review. Review the following code with the eye of someone who has maintained this codebase for two years and cares about long-term health over short-term cleverness.

Flag issues in these categories (prioritize in this order):
1. Logic errors or edge cases that could cause bugs in production
2. Security vulnerabilities (even minor ones)
3. Design problems — functions doing too much, wrong abstraction level, naming that misleads
4. Performance concerns (only flag if they'd matter at scale)
5. Style issues (lowest priority — flag only if they'd confuse a new team member)

For each issue: state the category, describe the problem in one sentence, and suggest a fix.

Code to review:
[PASTE YOUR CODE HERE]

Context: [describe what this code does and where it runs]

What this does: Structures the review output by impact priority — so you fix the critical things first and don't spend equal energy on a naming nitpick and a SQL injection risk.

Understanding the Variables

The prompt above has four moving parts worth understanding before you customize it.

The "two-year maintainer" framing — this is the most underrated element. It shifts feedback from "is this code correct right now?" to "will this code hurt someone in six months?" That changes what gets flagged. A function that works but has no error handling looks fine to a correctness-only reviewer. A maintainer knows it's a future 2am incident.

The priority order — most AI code reviews treat all feedback as equal. Putting logic errors first and style issues last trains the AI to model the same triage your team should be doing. I'm not 100% sure why, but unordered prompts consistently bury critical issues between formatting suggestions.

"Context" field — without it, the AI reviews code in a vacuum. "This runs as a Lambda function invoked by an SQS queue with a 30-second timeout" changes what performance and error-handling feedback looks like completely.

"Suggest a fix" — optional but valuable. Without it you get diagnosis only. With it you get diagnosis + a starting point, which is faster to act on.

⚡ Pro tip: Add your team's specific concerns as a numbered item. If your team has had three incidents related to database connection pooling, add "6. Database connection handling" to the list. The AI will look for it specifically.

Step-by-Step: Running AI Code Reviews

Step 1: Set context before pasting code. Don't paste code first and explain later. The AI uses early context to frame everything that follows. Language, framework, scale expectations, and team conventions all go at the top.

Step 2: Scope the review. Full-file reviews produce unfocused feedback. For a 200-line PR, review by logical section. For a 20-line function, the full prompt above works well. For larger PRs, try:

Focus this review on the authentication middleware only. Ignore the helper utilities at the bottom — those are unchanged legacy code and out of scope.

What this does: Prevents the AI from diluting feedback across irrelevant code.

Step 3: Ask for severity ratings. Add "Rate each issue: Critical / Major / Minor" to any review prompt. This makes it trivial to filter feedback and communicate priority to the PR author.

Rate each issue you find as Critical (fix before merge), Major (fix this sprint), or Minor (fix when you're in the area).

What this does: Gives you a triage output instead of a flat list.

Step 4: Run a focused security pass separately. General code review prompts underweight security. Run a second prompt dedicated to it:

Review this code specifically for security vulnerabilities. Check for: injection risks, authentication/authorization gaps, insecure data handling, hardcoded secrets, and improper error exposure. For each finding, rate severity as High / Medium / Low and cite the specific line or pattern.

Code: [paste]
Stack: [language, framework, where this runs]

What this does: Forces a security-specialist lens that a general review prompt won't apply consistently.

⚠️ Common mistake: Using AI code review as a replacement for human review rather than a pre-filter. AI is excellent at catching mechanical issues and pattern violations. It's much weaker at understanding team context, business logic, and the reasoning behind past decisions. Use it to clean up the easy stuff so your human reviewers can focus on the hard stuff.

⚡ Pro tip: Run the AI review prompt before submitting a PR, not just when reviewing others' code. Self-review catches 40–60% of issues before a teammate even sees the code — which makes you look more thorough and shortens the review cycle.

Pro-Level Variations

For TypeScript/React front-end PRs:

Review this React component with focus on: prop drilling depth, unnecessary re-renders (look for object literals or functions created inline in JSX), missing memoization, and TypeScript type accuracy. Ignore CSS/styling entirely.

For SQL queries:

Review this SQL query for: correctness of joins (check for accidental Cartesian products), index usage (flag full table scans on large tables), N+1 risks if this runs inside a loop, and injection safety if any values are user-supplied.

For infrastructure/Terraform code:

Review this Terraform configuration for: security group rules that are too permissive, resources without tagging, missing deletion protection on stateful resources, and hardcoded values that should be variables.

Troubleshooting Common Issues

Problem: AI feedback is too surface-level. Fix: Add "Do not comment on formatting, indentation, or style. Focus exclusively on logic, design, and security."

Problem: AI flags things that are intentional. Fix: Add a "Known exceptions" section. "We intentionally do X because Y — do not flag this pattern."

Problem: Output is too long to act on. Fix: Add "Limit your response to the top 5 most important issues only."

Problem: AI misunderstands the codebase conventions. Fix: Prepend a brief conventions block. "Our conventions: we use repository pattern, prefer composition over inheritance, and always validate at the boundary layer."

⚡ Pro tip: Build a "team review checklist" from your most common PR comments. Add it to your review prompt as a numbered list. This converts institutional knowledge — the mistakes your team makes repeatedly — into a systematic check that runs on every PR, not just when the developer who knows about that pattern is available.

Your Turn

One dimension most AI code review guides skip: reviewing for operational concerns, not just code quality. Good code can still be hard to operate. Add this as an optional category to your review prompt:

Also check for operational readiness: Does this code log enough information to debug if it fails in production? Are errors handled in a way that gives operators actionable context? Are there any places where a failure could be silent (no error thrown, no log written)?

What this does: Brings an SRE perspective into the code review process — catching the logs-and-observability gaps that only show up at 2am during an incident.

Start with the quick-start prompt at the top of this guide. Run it on your next PR before you submit. Count how many issues you catch that you wouldn't have spotted solo.

⚡ Pro tip: For any code review prompt you use frequently, ask the AI to generate a one-page review summary template: a structured document with sections for critical issues, design concerns, and minor suggestions. Having a consistent review output format makes it easier to discuss findings with PR authors — and makes your reviews more thorough by giving the AI a structured output to fill.

Teams that systematize this — building a shared review prompt for their specific stack — save real time. PromptABCD is useful here: store your team's tuned ai prompts for code review so everyone runs the same quality bar, not just whoever remembered to use AI that day.

Building a Team Review Standard

Individual code review prompts are useful. Team-standardized review prompts are transformative. When every developer on your team runs the same review prompt on their own code before submitting a PR, you get a consistent quality floor — not dependent on who happens to review that week.

The practical way to do this: pick your core review prompt, add your team's specific concerns (the kinds of issues that have caused incidents), and store it in a shared prompt library. Review it quarterly and update it when a new incident type appears. PromptABCD makes it easy to keep your ai prompts for code review versioned and accessible — so the prompt that prevents the next incident is always one click away.

Measuring Your AI Review ROI

Track two numbers over 30 days: issues caught by AI pre-submission, and issues caught by human reviewers post-submission. The ratio tells you how effective your current review prompts are. If human reviewers are still catching 80% of issues, your AI prompt needs tightening. If AI pre-filters 70%+ of issues, your team's human review time shifts to the high-value architectural conversations that AI can't replace.

Most teams that systematize AI code review see the human review session duration drop by 30–50% within a month — not because the AI catches everything, but because it handles the repetitive pattern violations that used to eat review time. That's time back in the day for the thinking work that actually requires human judgment. The best teams use AI to eliminate the predictable friction so that human review time goes entirely toward the unpredictable, high-judgment decisions — the trade-offs, the architectural concerns, the business context — that no prompt can evaluate.