Every production RAG system has the same core layers. Here's the full architecture map and what decisions each layer forces.
Retrieval-Augmented Generation (RAG) is the architectural pattern that lets an LLM answer questions using your data. Here's what it actually is and what it isn't.
RAG isn't always the right answer. Here are the scenarios where it adds complexity without improving outcomes and what to use instead.
Fine-tuning teaches a model new behaviors. RAG gives a model new facts. Most of what teams think they want from fine-tuning is actually a RAG problem.