What people are not always considering is that RAG has many more applications than just selecting the relevant context chunks. After all, the R in RAG is not fo

What people are not always considering is that RAG has many more applications than just selecting the relevant context chunks. After all, the R in RAG is not for Vector Search, it is for "Retrieval". With RAG tools that exist today, we can already do things like - providing summaries - hierarchical summarization - generation of questions / more prompts to nudge the model - caching - using knowledge graphs, function calling, or database connectors for non-semantic data querying etc.