1575 articles
Learn how to integrate LLM calls into microservice architectures with async patterns, job queues, and service contracts that don''t introduce latency bottlenecks.
Route queries intelligently to cheaper or more capable models based on complexity, intent, and latency SLAs, saving 50%+ on LLM costs while maintaining quality.
Master end-to-end LLM observability with OpenTelemetry spans, Langfuse tracing, and token-level cost tracking to catch production issues before users do.
Implement comprehensive LLM observability with LangSmith/LangFuse integration, token tracking, latency monitoring, cost attribution, quality scoring, and degradation alerts.
Implement exact-match and semantic caching with Redis to dramatically reduce LLM API calls, improving latency and cutting costs by 60% through intelligent cache invalidation.