Integrating LLM APIs: A Practical Guide for Developers

This post details practical engineering challenges when integrating LLM APIs, focusing on cost and latency optimization. It covers choosing LLM providers and access models, emphasizing the trade-offs between quality, speed, and cost. Key engineering strategies discussed include implementing prompt caching and semantic caching (leveraging Redis for low-latency vector search and LangCache), managing rate limits with effective retry strategies (exponential backoff and jitter for 429/5xx errors, avoiding retries for 400/401), and handling session state with Redis Agent Memory for context across turns and sessions. The post also introduces the concept of a GenAI gateway for shared infrastructure and observability.