AI-Assisted Development
Code Review with AI: A Second Pair of Eyes That Never Tires
A developer submits 500 lines of code for review. The colleague finds 20 minor comments: style, naming, a potential null. Time wasted discussing the obvious. AI finds those 20 things in 30 seconds, they get fixed, and the colleague spends time only on what matters - architecture and logic.
- **GitHub Copilot Chat**: A built-in reviewer right inside the IDE
- **Pre-commit hooks**: AI review automatically before every commit
- **PR automation**: Bots that review PRs and leave comments
- **Self-improvement**: Learn from AI's feedback to continuously improve code
Предварительные знания
AI as the Tireless Reviewer
A human reviewer loses focus after 200–400 lines of code. AI doesn't. That makes AI the ideal **first reviewer** - one that catches obvious problems before the human review.
**AI review catches:** - Style issues - Potential bugs (null, edge cases) - Security issues (SQL injection, XSS) - Performance anti-patterns - Best practice violations
**AI does NOT replace human review for:** - Architectural decisions - Business logic - Conformance to requirements - Project context (naming conventions, patterns)
When is AI review most useful?
Prompts for Code Review
A generic 'check the code' prompt produces weak results. **Specialized prompts** yield specific, actionable feedback.
**Code review template:** ``` # Context [Language, framework, what the code does] # Review focus [What to look for] # Code [Code to review] ```
**Useful review focuses:**
- **Security focus:** 'Find OWASP Top 10 vulnerabilities'
- **Performance focus:** 'Find bottlenecks and O(n²) algorithms'
- **Maintainability:** 'What will be hard to understand in 6 months?'
- **Testing:** 'What tests are needed for full coverage?'
- **Refactoring:** 'Where does this violate SOLID/DRY?'
You want to check payment code for security. Which prompt is better?
Specialized Review Types
Different situations call for different types of review. Here are battle-tested prompt templates:
**Security Review:** 'As a security expert, review the code for: - Injection (SQL, command, LDAP) - XSS and CSRF - Broken authentication - Sensitive data exposure - Missing input validation For each issue: line, severity (critical/high/medium/low), exploit scenario, fix.'
**Performance Review:** 'Analyze for performance: - Algorithmic complexity (Big O) - N+1 queries - Memory leaks / unbounded growth - Blocking operations in async code - Caching: what could be cached? Assume load: 1,000 concurrent users, 1M rows in the DB.'
**Pre-commit Review:** 'Quick review before committing: - Any debug code? (console.log, print, debugger) - Commented-out code? - TODO/FIXME without an issue link? - Hardcoded secrets? - Unused imports/variables? Format: file:line - issue'
Before a production deploy, a quick final check is needed. What type of review?
AI Review of Your Own Code
The most valuable use of AI review is **checking code before opening a PR**. Problems are caught before colleagues even see them.
**Benefits of self-review with AI:** 1. Find bugs before human review 2. Improve code before it's published 3. Learn from AI's comments 4. Save reviewers' time
**Trap:** Don't accept everything AI says. Sometimes AI comments are over-engineering or irrelevant to the project's context. Critically evaluate suggestions.
**The 'Rubber Duck + AI' pattern:**
AI review replaces human review
AI review complements human review by removing noise
AI doesn't understand business context, doesn't know the project's history, doesn't see architectural trade-offs. Human review remains critical for important decisions.
You finished a feature and want to do a self-review with AI. What's the right approach?
Key Takeaways
- **AI is the first reviewer**, humans do the deep review
- **Specialized prompts** produce specific, actionable results
- **Review types**: Security, Performance, Pre-commit, Legacy
- **Self-review with AI** before a PR saves everyone's time
- **Critically evaluate** AI suggestions - not all of them are relevant
What's Next?
Code review is a reactive process. Next - proactive use of AI:
- AI for Architecture — Discussing trade-offs before writing code
- Debugging with AI — How to describe bugs effectively
- Testing with AI — Generating tests and test cases
Вопросы для размышления
- What types of mistakes appear most often in a codebase? Consider creating a specialized prompt for them.
- Try 'Rubber Duck + AI': explain a recent piece of code to AI. What patterns emerge that weren't obvious before?
- How could AI review be integrated into a team's workflow?