“80% of my code is written by AI, 20% is spent reviewing and correcting it.” — Andrej KarpathyThe 80/20 review pattern optimizes this ratio. Instead of reviewing every edit, batch reviews at strategic checkpoints. Between checkpoints, trust the AI and move fast.
The Problem with Constant Review
Reviewing every change kills momentum:- Context switching overhead
- Interrupts AI’s flow
- Reviews often find nothing wrong
- Slows down iteration speed
The 80/20 Solution
Batch reviews at checkpoints:- 33% faster overall
- Better context (see changes together)
- AI maintains flow between reviews
- Reviews are more thorough (not rushed)
Review Checkpoints
Review at these strategic points:Checkpoint 1: After Plan Approval
When: Before any implementation begins What to review:- Does the plan make sense?
- Are all affected files identified?
- Are risks and mitigations reasonable?
- Is the approach aligned with project patterns?
This is the most important review point. A bad plan leads to many corrections later.
Checkpoint 2: Every 5 File Edits
When: After editing 5 files (or ~20 minutes of work) What to review:- Are changes following the plan?
- Any obvious bugs or logic errors?
- Tests passing?
- Ready to continue or need course correction?
Checkpoint 3: Before Destructive Operations
When: Before operations that are hard to undo Destructive operations:git commit(review before committing)git push(double-check before pushing)- File deletion (verify files should really be deleted)
- Schema changes (migrations are hard to reverse)
npm publish(can’t unpublish)
Checkpoint 4: At Wrap-Up
When: Before ending the session What to review:- All changes across the session
- Uncommitted work
- TODOs or console.log statements
- What was learned?
- Any corrections to capture?
/wrap-up command:
Between Checkpoints: Trust Mode
Between review checkpoints, operate in trust mode:Let AI work without interruption
Run tests automatically after each file
Watch for test failures (pause if tests break)
Trust the plan and process
Quality Gates
Automatic quality gates run between checkpoints:- After Each File Edit
- Every 5 Edits (Checkpoint)
- Before Commit (Checkpoint)
Hook Integration
Pro Workflow hooks enforce checkpoint reviews: PreToolUse Hook (tracks edit count)Review Checklist
What to look for at each checkpoint:Logic & Correctness
Logic & Correctness
- Logic matches the plan
- Edge cases handled
- Error cases handled
- No obvious bugs
- Calculations and conditions correct
Code Quality
Code Quality
- Follows existing patterns
- Clear variable and function names
- No unnecessary duplication
- Comments for non-obvious logic
- No debug statements (console.log, debugger)
Security
Security
- No hardcoded secrets
- Input validation present
- SQL injection prevention
- XSS prevention
- Authentication checks
Testing
Testing
- Tests passing
- New tests for new code
- Edge cases tested
- Error cases tested
- Coverage maintained or increased
Performance
Performance
- No obvious N+1 queries
- Large loops optimized
- Appropriate indexes (database)
- Caching where needed
- No memory leaks
When to Pause Mid-Flow
Sometimes you need to pause before the next checkpoint: Pause if:- Tests suddenly break
- Error messages you don’t understand
- AI asks a clarifying question
- Approach seems wrong
- About to make a destructive change
- Stylistic preferences (add to LEARNED instead)
- Minor improvements (note for later)
- Curiosity about implementation (trust the process)
Correction Workflow
When you find an issue at a checkpoint:Metrics and Analytics
Track your review effectiveness:- Corrections per checkpoint: Should decrease over time
- Issues found: More at early checkpoints vs late (front-load validation)
- Time between checkpoints: Should be consistent (5 files or ~20 min)
- Tests catching issues: High number means good test coverage
Comparison: Constant vs Checkpoint Review
| Factor | Constant Review | Checkpoint Review (80/20) |
|---|---|---|
| Speed | Slow (many interruptions) | Fast (sustained flow) |
| Context | Lost between reviews | Preserved between checkpoints |
| Thoroughness | Rushed, shallow | Batch review is deeper |
| AI Flow | Interrupted | Maintained |
| Time to completion | 40-60% longer | Baseline |
| Corrections found | Same | Same (batched) |
Best Practices
Do
Review the plan thoroughly before implementation
Pause every 5 file edits for checkpoint review
Run quality gates automatically
Trust the process between checkpoints
Capture corrections as learnings
Use /wrap-up for final review
Don’t
Integration with Other Patterns
Multi-Phase Development- Each phase has its own review checkpoint
- Research phase: review confidence score
- Plan phase: review plan before implementation
- Implement phase: review every 5 edits
- Final phase: wrap-up review
- Corrections found at checkpoints → [LEARN] rules
- Rules prevent same issues at future checkpoints
- Checkpoints become faster over time
- Each teammate reports at checkpoints
- Lead reviews all teammate outputs together
- Synchronization points prevent conflicts
Real-World Example
Example: Feature Development with Checkpoints
Example: Feature Development with Checkpoints
Adjusting Checkpoint Frequency
Tune checkpoint frequency based on complexity:| Task Complexity | Checkpoint Frequency |
|---|---|
| Simple, well-understood | Every 7-10 files |
| Moderate complexity | Every 5 files (default) |
| High complexity, new domain | Every 3 files |
| Critical (auth, payments) | Every 1-2 files |
| Experimentation | Every 10+ files (trust mode) |
Next Steps
Self-Correction Loop
Capture review findings as learnings
Multi-Phase Development
Apply checkpoints in structured workflows