TL;DR: AI can review code in three ways that classic linters cannot: detecting logical bugs and security vulnerabilities, refactoring for actual readability, and generating complete unit tests. With the right prompts, you cut review time in half and spot problems that no static linter captures.
Why Code Review with AI Is Not the Same as Linting
Linters like ESLint, Pylint, or SonarQube are excellent tools. They detect syntax errors, style violations, unused variables, and high cyclomatic complexity. But they have a fundamental limit: they analyze code statically, without understanding intent.
AI changes the kind of feedback you can get. A language model can:
- Identify a race condition that only occurs under specific concurrency conditions
- Detect that a function does too many things even though it is technically "correct"
- Suggest that a variable name is confusing in the context of the business domain
- Point out that a block of code assumes undocumented behavior of a library
This does not replace linting — it complements it. The ideal workflow combines automated static tools with semantic reviews assisted by AI.
5 Specific Techniques and Prompts
1. Detecting Bugs and Security Vulnerabilities
This is the most valuable use case. AI can identify logical problems, race conditions, SQL injections, incorrect error handling, and many other vulnerabilities that static linting misses.
Recommended Prompt:
Review this [language] code looking for:
1. Logical bugs and unhandled edge cases
2. Race conditions or concurrency issues
3. Security vulnerabilities (injection, data exposure, authentication)
4. Incorrect error handling or silenced exceptions
5. Implicit assumptions about inputs that could fail in production
For each issue found, explain: what it is, why it is a problem, and how to fix it.
[PASTE YOUR CODE HERE]
Tip: Be specific about context. If the code handles user data, add "this code processes personal user data" — the model will prioritize relevant privacy and security concerns.
2. Refactoring for Readability
Code that works is not necessarily maintainable. AI can transform dense functions into code that any developer on the team can understand in 30 seconds.
Recommended Prompt:
Refactor this code to improve its readability and maintainability without changing its functionality:
- Separate responsibilities into smaller functions if appropriate
- Improve variable and function names so they are self-descriptive
- Eliminate comments that explain "what" (this should be obvious in the code) and keep those that explain "why"
- Add type hints (Python) or explicit types (TypeScript)
- Extract duplicated logic if present
Show me the refactored code and a brief list of the most important changes you made and why.
[PASTE YOUR CODE HERE]
What to expect: AI is exceptionally good at extracting helper functions with descriptive names and simplifying complex conditional logic. Results are most useful when the original code is between 30 and 150 lines.
3. Optimizing Performance
Before optimizing, identify what to optimize. AI can point out the most obvious bottlenecks and suggest alternatives with better algorithmic complexity.
Recommended Prompt:
Analyze this code looking for performance issues:
1. Operations with improvable algorithmic complexity (O(n²) that could be O(n log n), etc.)
2. Database or network calls inside loops (N+1 queries)
3. Inefficient use of data structures
4. Repeated calculations that could be cached
5. Synchronous operations that could be asynchronous
For each issue: show the current code, explain the performance issue with an example of a low vs. high-load scenario, and propose the optimized alternative.
Context: [briefly describe the expected data volume and context of use]
[PASTE YOUR CODE HERE]
Important: Always add scale context. "This endpoint receives 10 requests/day" vs. "it processes 100,000 records nightly" changes completely which optimizations make sense.
4. Generating Unit Tests Automatically
Writing tests is tedious and often postponed. AI can generate a complete test suite, including edge cases that are frequently forgotten.
Recommended Prompt:
Generate comprehensive unit tests for this function using [pytest/Jest/the framework you use]:
Include tests for:
- The happy path with typical inputs
- Boundary values (0, null, empty string, empty arrays, maximum values)
- Invalid inputs and how the function should handle them
- Specific domain edge cases (describe the context if relevant)
- Necessary mocks for external dependencies (DB, APIs, filesystem)
Use descriptive names for each test explaining what is being verified and under what condition.
[PASTE YOUR CODE HERE]
Additional Tip: After generating the tests, ask: "What additional edge cases do you think I should cover and why?". It often suggests non-obvious scenarios related to the business domain.
5. Documenting Existing Code
Outdated documentation is worse than no documentation. AI can generate docstrings, JSDoc comments, or complete module documentation and keep it synchronized with the code.
Recommended Prompt:
Generate complete documentation for this code:
- Docstrings/JSDoc for each function and class with: description, parameters (name, type, description), return value, exceptions it can throw, and a usage example
- Inline comments for non-obvious logic (only where it truly adds clarity)
- If it is a full module, add a documentation block at the beginning explaining the purpose, main dependencies, and how it is used
Use the standard format of [Python/TypeScript/the language you use].
[PASTE YOUR CODE HERE]
Recommended Tools for AI Code Review
Cursor Composer
The best option if you want review + code modification in a single workflow. You can select a file or folder, open Composer, ask it to do the review, identify problems, and apply fixes directly. The Agent mode can run tests after changes to verify nothing is broken.
GitHub Copilot Chat
Ideal if you already use Copilot and want inline reviews. Select a block of code, right-click → "Copilot" → "Review and Comment." It also works well with the /fix command for minor errors and /tests for quick test generation.
Claude (claude.ai or API)
For more complex reviews where you want to paste broad context — multiple files, API documentation, business requirements — Claude stands out for its large context window and ability to reason about complete systems. It is especially useful for detecting architectural issues that go beyond single functions.
Limitations: What AI CANNOT Do in Code Reviews
Being honest about limitations is as important as knowing the capabilities.
AI does not know your business context unless you explain it. It can tell you a function is syntactically correct when it actually violates a critical business rule that is not documented in the code.
False positives are real. AI sometimes flags "problems" that are actually intentional design decisions. Always critically evaluate every suggestion.
It does not replace an expert human reviewer in your domain. A security expert will see threat model vulnerabilities that AI does not consider. An architect will see scalability issues in the design that go beyond code syntax.
AI-generated code needs human review. If you use AI to generate code, you need an additional review process — precisely because the model can generate code that looks correct but has subtle bugs.
Output quality depends on prompt quality. A vague prompt yields generic feedback. The more specific context you provide, the more useful the review is.
Recommended Workflow
- Automated linting first — let static tools catch basic formatting and syntax errors.
- AI review for semantic context — use the prompts from this guide.
- Apply suggested changes selectively — not in automatic mode.
- Run tests — including those generated by AI.
- Final human review for architectural decisions and business context.
Ir a la herramienta
Frequently Asked Questions
Is it safe to paste proprietary code into AI tools?
It depends on the tool and the plan. Claude, ChatGPT, and most consumer tools do not use your code for training in their paid APIs. Codeium has an explicit non-training policy. For highly sensitive code (keys, personal data, core algorithms), use tools with verified privacy agreements or local models like CodeLlama. Never paste real credentials, tokens, or personal data.
How much code should I include in each prompt?
The optimal range is 50-200 lines. With less context, feedback is too generic. With more, the model can lose focus on important parts. For large files, divide by modules or functions and review in chunks. If you need multi-file context, Cursor Composer is the best option.
Can I integrate this into my CI/CD?
Yes, using APIs from OpenAI, Anthropic, or tools like CodeRabbit and Graphite. You can configure a CI step that automatically reviews each PR and comments directly on GitHub. For teams that want this without custom configuration, CodeRabbit offers direct integration with GitHub and GitLab.