Browsing: Rag Architecture

Retrieval-Augmented Generation (RAG) is the most widely used LLM use case across organizations. By vectorizing documents and retrieving semantically similar chunks at query time, RAG mitigates hallucinations, grounds responses and bypasses static knowledge cutoffs imposed by model pretraining. However, in a real-life scenario, standard vector-based RAG runs into limitations for complex queries, such as those…