Optimizing JetBrains AI-Generated Code for Performance

Generated Java/Kotlin code frequently has N+1 query problems — fetching a list and then querying each item individually. JPA generated code may load entire entity graphs when only a few fields are needed. Java stream pipelines are sometimes unnecessarily complex with multiple intermediate operations that could be combined

Use JPA fetch joins or @EntityGraph to load related data in one query. Select DTOs with specific fields instead of fetching full entities. Profile with JProfiler or async-profiler to find hot spots in generated code. Use Hibernate's statistics to identify N+1 queries

Before optimizing, measure. Use your browser's Lighthouse audit to get a baseline score. Check Core Web Vitals (LCP, FID, CLS) — these directly affect your Google ranking. Use the Performance tab in DevTools to identify which components take the longest to render. For server-side performance, add timing logs to API routes to find slow endpoints.

The fastest way to improve performance: optimize and lazy-load images, enable compression on your server, remove unused CSS and JavaScript, implement code splitting so users only download code for the page they're viewing, and add proper caching headers for static assets. These changes alone can cut load times significantly.

If your JetBrains AI app uses a database, slow queries are often the biggest performance bottleneck. Add indexes on columns you filter or sort by. Use SELECT to fetch only the columns you need instead of SELECT *. Implement pagination for lists — loading 10,000 rows at once will always be slow. Consider adding a caching layer for data that doesn't change frequently.