Can prompt caching tame RAG costs without sacrificing accuracy?
The New Stack Emmanuel Akita
A RAG architecture tutorial examines three production bottlenecks: synchronous ingestion causing timeouts and cascade failures (fixed with batched fan-out asynchronous pipelines), logical multi-tenancy creating security and performance risks (fixed with serverless namespaced isolation), and semantic caching returning stale answers across similar queries (fixed with hybrid intent routing or infrastructure-level prompt caching). Prompt caching at the LLM provider level can reduce context token costs by up to 80% and drop time-to-first-token to milliseconds. The shift from treating RAG as plug-and-play to engineering it as a distributed systems problem determines whether a prototype survives production deployment in enterprise SaaS environments.
Why it matters
The AI ecosystem is drowning in tutorials on how to build a retrieval-augmented generation (RAG) app in five minutes. The The post Can prompt caching tame RAG costs without sacrificing accuracy? appeared first on The New Stack.
