LLM Wiki - Compiled Knowledge That RAG Cannot Replace

The premise is simple: compiled knowledge is more reusable than retrieved fragments. RAG became the default answer to a straightforward question - how do I give an LLM access to external knowledge? And the usual architecture is by now familiar. Take documents, split them into chunks, embed the chunks, store them in a vector database, retrieve relevant pieces at query time, and pass them into the model. That pattern is useful, but it is also overused. RAG is very good at access and not automat...