Finding the Right Answers from Thousands of Documents: A Smarter RAG Approach

Author(s): Shrinidhi Atmakur Originally published on Towards AI. Finding the Right Answers from Thousands of Documents: A Smarter RAG Approach Introduction RAG is often presented as a simple, three-step architecture: put documents into a vector database, convert the user’s question into an embedding, retrieve a handful of chunks, and hand them to an LLM. That approach is a great proof of concept. It is also where most RAG projects quietly stall. But what happens when the knowledge base grows to

Original reporting by Towards AI · Shrinidhi Atmakur. GridIndex is an aggregation and intelligence layer — full credit to the original publisher.