How RAG Is Maturing: Choosing the Right Retrieval Pipeline for Your Use Case Retrieval-Augmented Generation (RAG) has moved far beyond the “experimental” phase. In the early days, RAG was simply about feeding a PDF into a vector database and asking a chatbot to summarize it. Today, the industry is shifting toward Compound AI Systems , where RAG is no longer a single step but a complex, multi-layered cognitive architecture. As we scale these systems for the enterprise, the “Naive” approach is failing. To solve for accuracy, latency, and complex reasoning, we are seeing the emergence of specialized RAG patterns. Here is the blueprint for how RAG is maturing and how to choose the right architecture for your pipeline. Press enter or click to view image in full size Generated by AI Why RAG Keeps Evolving Basic RAG splits documents into fixed chunks, embeds them, retrieves the top k by cosine similarity, and passes them to an LLM. This works for simple FAQs but breaks on exact identifiers, l...
