performance

Identifying performance bottlenecks across frontend rendering, API latency, and LLM inference requires a targeted performance audit. This process analyzes Core Web Vitals, render patterns, and API latency to pinpoint slow areas and provide actionable optimization steps.

Optimizing Core Web Vitals and reducing React bundle size involves applying code-splitting, lazy loading, and image optimization. These frontend render improvements decrease load times and produce a faster, more efficient user experience.

Speeding up API endpoints using caching strategies and backend performance tuning involves implementing Redis caching and compression middleware. These techniques reduce server latency and minimize redundant data processing for faster API response times.

Optimizing LLM inference throughput with vLLM quantization and speculative decoding involves applying production-grade deployment patterns. These efficient LLM deployment techniques reduce model memory usage and accelerate token generation for AI workloads.

You can use this performance optimization guidance with your existing Express and React product engineering workflow. It produces actionable steps, success criteria, and tooling recommendations tailored for typical product engineering workflows.