RAG in Production: Reliability, Latency, and Cost for LLM Apps
Run retrieval-augmented generation at scale. Chunking, caching, and observability.
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RAG (retrieval-augmented generation) powers many LLM apps. Here’s how to run it reliably in production.
Architecture Basics
- Ingest: Chunk documents, embed, store in a vector DB.
- Query: Embed query, retrieve top-k, optionally re-rank, then prompt LLM with context.
Reliability
- Retries with backoff for embedding and LLM APIs.
- Fallbacks (e.g. cached answer or “try again”) when retrieval or LLM fails.
- Timeouts so one slow call doesn’t block the whole request.
Latency
- Cache frequent queries or embeddings where safe.
- Async embedding for ingest; keep query path synchronous and fast.
- Re-ranking: Use a small re-ranker only when needed to balance quality and latency.
Cost
- Chunk size and top-k affect token usage; tune for quality vs cost.
- Model choice: Smaller or quantized models for simple tasks; reserve larger models for hard queries.
Best practice: add metrics (latency p95, cache hit rate, cost per query) and alerts so you can iterate.
Best Practices: AI Inference Cost Optimization
We cut LLM inference cost 47% over a quarter while improving p95 latency. Six changes, ranked by what each one actually delivered.
Building RAG Applications: A Complete Guide to Retrieval Augmented Generation
We've shipped four production RAG applications. Each one taught us something. The end-to-end pattern that works.
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