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 hundreds or thousands of documents? Retrieval becomes more challenging, irrelevant chunks can reach the LLM, token consumption increases, and the quality of the final answer becomes increasingly dependent on retrieval quality. This article shares information on building a production-ready, multi-stage RAG pipeline that can scale without simply sending more and more context to the LLM. RAG in Two Lines Retrieval-Augmented Generation allows an AI model to answer questions using external knowledge. Instead of relying only on the LLM’s internal knowledge, the system retrieves relevant information from a knowledge base and provides it to the model as context before generating an answer. User Que…

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