Building a semantic cache layer in front of RAG — and why it might be the most underrated cost...
Building a semantic cache layer in front of RAG — and why it might be the most underrated cost optimization in production AI systems.
Hey Dev community 👋
Every RAG architecture diagram I see looks exactly the same.
User → Vector Search → LLM → Response.
But I keep wondering...
RAG sounds complicated. It's not. But a lot of introductions to RAG make it sound more mysterious...
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency...
RAG (Retrieval-Augmented Generation) is the foundation of knowledge-grounded AI. But most RAG...
Key Takeaways Most SaaS AI agents don't need a vector database — file-based memory plus 1M-token...
RAG is not new. Chunk a document, embed the chunks, store them in a vector database, run a retrieval...
Why modern AI coding agents often use grep, file reads, symbols, and tests before reaching for vector RAG.
