As part of my AI Engineering journey, I recently worked on a project that helped me understand how Retrieval-Augmented Generation (RAG) works in practice.
I built a Hybrid RAG system that combines FAISS vector search and BM25 keyword search to retrieve relevant information from a knowledge base and use it to generate grounded answers.
In this post, I’ll briefly share what I built, how the system works, and some of the things I learned along the way.
Why RAG?
Large Language Models are great at generating natural-language responses, but they may not have access to information contained in a specific document or knowledge base.
