Adaptive RAG
No, RAG cannot solve all our problems and challenges. RAG, just like any generative model, can also produce irrelevant and incorrect output! RAG might be a useful option, however, because we feed pertinent documents to the generative AI model that inform its responses. Nonetheless, the quality of RAG outputs depends on the accuracy and relevance of the underlying data, which calls for verification! That’s where adaptive RAG comes in. Adaptive RAG introduces human, real-life, pragmatic feedback that will improve a RAG-driven generative AI ecosystem.
The core information in a generative AI model is parametric (stored as weights). But in the context of RAG, this data can be visualized and controlled, as we saw in Chapter 2, RAG Embedding Vector Stores with Deep Lake and OpenAI. Despite this, challenges remain; for example, the end-user might write fuzzy queries, or the RAG data retrieval might be faulty. An HF process is, therefore, highly recommended to ensure...
