When I started learning RAG, I found plenty of tutorials using frameworks, but I struggled to understand what was actually happening behind the scenes.
I wanted to understand the complete process, from taking a document to retrieving relevant information and generating an answer.
So after learning it myself, I decided to make the tutorial I wish I had found earlier.
I start with a basic RAG pipeline using Python, Scikit-learn and FAISS. I explain chunking, TF-IDF, sparse matrices, normalization, cosine similarity and retrieval.
Then I gradually move into advanced RAG techniques like query rewriting and reranking, followed by modular RAG with routing, query fusion and Reciprocal Rank Fusion.
I tried to explain not just how to implement each part, but why it exists.
The tutorial is for anyone who knows basic Python and wants to understand RAG from the ground up.
When I started learning RAG, I found plenty of tutorials using frameworks, but I struggled to understand what was actually happening behind the scenes.
I wanted to understand the complete process, from taking a document to retrieving relevant information and generating an answer.
So after learning it myself, I decided to make the tutorial I wish I had found earlier.
I start with a basic RAG pipeline using Python, Scikit-learn and FAISS. I explain chunking, TF-IDF, sparse matrices, normalization, cosine similarity and retrieval.
Then I gradually move into advanced RAG techniques like query rewriting and reranking, followed by modular RAG with routing, query fusion and Reciprocal Rank Fusion.
I tried to explain not just how to implement each part, but why it exists.
The tutorial is for anyone who knows basic Python and wants to understand RAG from the ground up.
Video: https://www.youtube.com/watch?v=aenpTk8blIs