Author(s): Shrinidhi Atmakur Originally published on Towards AI. Finding the Right Answers from Thousands of Documents: A Smarter RAG ApproachIntroductionRA ...
Author(s): Shrinidhi Atmakur
Originally published on Towards AI.
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.
Introduction Here is a scenario many RAG builders know well, you wire up a pipeline, load...
RAG is not new. Chunk a document, embed the chunks, store them in a vector database, run a retrieval...
Want to build an AI assistant that talks to your company documents? First you need to answer one...
Author(s): GOWRI SHANKAR RAJU Originally published on Towards AI. Data Engineering for RAG: Building Reliable AI with Better Data…
The tutorial-to-production gap Every RAG tutorial follows the same arc. Load some...
Every company sits on a mountain of internal knowledge — contracts, technical manuals, compliance...
