Optimizing GPT Engineer-Generated Code for Performance

No caching, no pagination, no lazy loading. Every page load fetches all data. Images at full resolution. The full application JavaScript loads on initial page load

Add pagination for data lists. Implement image optimization. Add code splitting. Configure proper caching headers. Lazy load non-critical components

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 GPT Engineer 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.