#performance-review.md
Version: 1.0.0
Target Models
- Mistral Medium 3.5
- Mistral Large 3
- Mistral Small 4
- Mistral Family
- Future Mistral Models
#Purpose
This document defines the Performance Review methodology for software engineering.
Performance review is the systematic evaluation of an application's efficiency, scalability, responsiveness, and resource utilization.
Performance is not measured by isolated benchmarks.
It is measured by how efficiently the entire system delivers value under realistic workloads.
The objective is to maximize performance while preserving correctness, maintainability, security, and developer experience.
#Core Philosophy
Measure
↓
Analyze
↓
Identify Bottlenecks
↓
Optimize
↓
Measure Again
↓
Verify
↓
Approve
Never optimize assumptions.
Always optimize measured evidence.
#Primary Objective
Every performance review should answer one question.
"If user traffic increases by 10x tomorrow, will this implementation continue to perform reliably?"
If the answer is uncertain,
the system requires further optimization.
#Performance Principles
Every optimization should improve
Responsiveness
↓
Resource Efficiency
↓
Scalability
↓
Reliability
↓
Predictability
↓
User Experience
↓
Operational Stability
Performance should never reduce correctness.
#Review Workflow
Understand System
↓
Measure Current Performance
↓
Identify Bottlenecks
↓
Analyze Root Causes
↓
Evaluate Trade-offs
↓
Optimize
↓
Measure Again
↓
Approve
#Stage 1 — User Experience Performance
Review
Initial Load Time
First Contentful Paint
Largest Contentful Paint
Interaction Delay
Input Responsiveness
Smooth Animations
Navigation Speed
Users judge performance through perception.
Optimize perceived speed first.
#Stage 2 — Frontend Performance
Inspect
Rendering
Re-render frequency
State updates
Component hierarchy
Memoization
Virtualization
Image optimization
Font loading
Lazy loading
Code splitting
Every unnecessary render wastes resources.
#Stage 3 — Backend Performance
Review
API latency
Business logic
Concurrency
Thread utilization
Async processing
Serialization
Compression
Caching
Connection pooling
Backend throughput determines scalability.
#Stage 4 — Database Performance
Inspect
Query execution
Indexes
Joins
Transactions
Pagination
N+1 queries
Locks
Connection usage
Database optimization should begin with query analysis.
#Stage 5 — API Performance
Review
Response size
Latency
Compression
Caching
Pagination
Streaming
Batch requests
Retries
Timeouts
Every API should minimize unnecessary communication.
#Stage 6 — Memory Performance
Review
Memory allocation
Object lifetime
Garbage collection
Memory leaks
Caching strategy
Data duplication
Memory fragmentation
Stable memory usage improves long-term reliability.
#Stage 7 — CPU Performance
Inspect
Heavy calculations
Nested loops
Recursive operations
Sorting
Searching
Parsing
Data transformation
Background processing
Optimize expensive computation first.
#Stage 8 — Network Performance
Review
Duplicate requests
Request waterfalls
Compression
Caching
CDN usage
Prefetching
Preloading
Keep-alive connections
Network latency often dominates user experience.
#Stage 9 — Asset Performance
Inspect
Images
Fonts
Icons
Videos
JavaScript
CSS
Third-party assets
Unused assets
Every downloaded byte affects performance.
#Stage 10 — Caching Review
Verify
Browser cache
CDN cache
Application cache
Database cache
API cache
Object cache
Session cache
Caching should improve performance without sacrificing consistency.
#Stage 11 — Scalability Review
Evaluate
Horizontal scaling
Vertical scaling
Load balancing
Stateless services
Queue systems
Worker processes
Distributed caching
Performance should improve as infrastructure grows.
#Stage 12 — Concurrency Review
Review
Parallel execution
Thread safety
Async workflows
Task scheduling
Lock contention
Race conditions
Resource contention
Concurrency should increase throughput without reducing stability.
#Stage 13 — Resource Utilization
Inspect
CPU
Memory
Disk I/O
Network
GPU
Database connections
File descriptors
Unused resources are hidden performance costs.
#Stage 14 — Mobile Performance
Review
Slow devices
Low bandwidth
Battery usage
Touch responsiveness
Animation smoothness
Offline capability
Performance should remain acceptable on lower-end hardware.
#Stage 15 — Build Performance
Review
Compilation speed
Bundle generation
Incremental builds
Tree shaking
Dependency graph
Developer tooling
Developer productivity is also performance.
#Stage 16 — Third-Party Review
Inspect
External APIs
Analytics
Tracking
Fonts
Widgets
SDKs
Unused integrations
Third-party services should justify their performance cost.
#Stage 17 — Stress Review
Evaluate
High traffic
Large datasets
Concurrent users
Slow databases
Network failures
Resource exhaustion
Traffic spikes
Performance should remain predictable under stress.
#Stage 18 — Monitoring Review
Verify
Metrics
Tracing
Logging
Profiling
Alerts
Dashboards
Performance budgets
What cannot be measured cannot be improved.
#Stage 19 — Regression Review
Confirm
No new bottlenecks
No memory regressions
No rendering regressions
No query regressions
No bundle growth
No degraded UX
Optimization should not introduce new problems.
#Measurement Strategy
Measure
↓
Benchmark
↓
Profile
↓
Optimize
↓
Benchmark Again
↓
Compare Results
↓
Approve
Every optimization should have measurable improvement.
#Performance Metrics
Review
Response Time
Latency
Throughput
Memory Usage
CPU Usage
FPS
Bundle Size
Cache Hit Rate
Database Query Time
Network Requests
Error Rate
System Availability
Metrics should guide engineering decisions.
#Performance Questions
Before approval ask
Are bottlenecks measured?
↓
Can unnecessary work be removed?
↓
Can existing work be reused?
↓
Can data move more efficiently?
↓
Will this scale with growth?
↓
Would users perceive this as fast?
#Severity Levels
Critical
Application freezes
Memory leaks
Database bottlenecks
System instability
Major
Slow rendering
Large bundles
Heavy queries
Excessive API latency
Medium
Redundant rendering
Minor caching issues
Inefficient algorithms
Minor
Small optimizations
Asset cleanup
Documentation improvements
Suggestion
Future optimization
Architecture improvements
Infrastructure enhancements
#Performance Checklist
✓ Frontend optimized
✓ Backend optimized
✓ Database reviewed
✓ APIs efficient
✓ Memory usage acceptable
✓ CPU usage acceptable
✓ Network optimized
✓ Assets optimized
✓ Caching configured
✓ Scalability verified
✓ Monitoring enabled
✓ No performance regressions
#Anti-Patterns
Avoid
Premature optimization
Benchmark-free optimization
Micro-optimizations
Over-engineering
Duplicate rendering
N+1 queries
Blocking operations
Unbounded memory growth
Large synchronous work
Ignoring performance measurements
#Definition of Done
Performance review is complete when
- Bottlenecks have been identified through measurement.
- User experience remains responsive.
- Resource utilization is efficient.
- APIs and database interactions are optimized.
- Memory and CPU usage remain stable.
- Network communication is minimized.
- The application scales predictably.
- Monitoring supports continuous improvement.
- No regressions have been introduced.
- The implementation achieves the required performance while preserving maintainability, security, and correctness.
Performance is not about making software fast.
Performance is about delivering consistent, efficient, and reliable experiences under real-world conditions while maintaining long-term engineering quality.