Performance Optimization With AI
AI coding assistants excel at performance analysis. They can interpret profiler output, spot inefficient patterns, explain complex performance behavior, and suggest targeted optimizations. The key is providing enough context for meaningful analysis.
Analyzing Slow Code
When you have slow code and profiler output, AI can connect the dots:
Here's my slow API endpoint code:
[paste the function]
And here's the profile showing where time is spent:
[paste profile output]
What optimizations would have the biggest impact?
AI can identify that 80% of time is spent in a function that makes database calls, suggest adding an index or restructuring queries, and explain why certain operations are expensive.
Include both the code and the profiling data. Code alone might look fine; the profile reveals where time actually goes.
Interpreting Load Test Results
Load test output can be confusing. AI helps make sense of the patterns:
My load test shows:
- p50: 200ms
- p95: 500ms
- p99: 5000ms
- Error rate increases above 500 RPS
What's causing the p99 to be so high compared to p50?
What should I investigate?
AI might explain that the huge gap between p50 and p99 suggests resource contention — perhaps database connection pool exhaustion or garbage collection pauses. It can recommend specific metrics to check and common causes to investigate.
Query Optimization
Database query optimization benefits enormously from AI analysis:
Review this database query for performance:
[paste query]
Here's the EXPLAIN output:
[paste explain results]
How can I make this faster?
AI can interpret the query plan, identify missing indexes, suggest query restructuring, and explain why certain operations are expensive. It might notice a sequential scan on a large table and recommend the specific index to add.
Architecture-Level Analysis
For broader performance concerns, describe your system architecture:
My application has:
- React frontend
- Node.js API server
- PostgreSQL database
- Redis for sessions
Users report slow page loads. The API responds in 50ms
but pages take 3 seconds to become interactive.
Where should I focus optimization efforts?
AI can identify that the bottleneck is likely frontend-related given the fast API, suggest investigating JavaScript bundle size, render-blocking resources, or client-side data fetching patterns.
Effective Performance Prompts
Be specific about symptoms: "slow" is vague; "p99 latency increased from 200ms to 2s after deploying feature X" is actionable.
Include metrics and measurements. Numbers let AI provide quantitative analysis rather than generic advice.
Describe what you've already tried. This prevents AI from suggesting things you've ruled out and focuses on fresh approaches.
Ask for explanations, not just solutions. Understanding why something is slow helps you prevent similar issues in the future.