Retrieval-Augmented Generation (RAG) — Basics to Advanced Series (Part 1)
Decoding the bigger picture !!
Pretext: I have been working on RAG build and efficacy assessments for a while and I realised there is certainly a lack of simplified resources available to understand the domain better. That’s why, I’ve am creaing this blog series, where the aim is to share my RAG journey lessons from scratch. If you come across any parts that need further explanation, please leave a comment.
Let’s dive in and unravel the mysteries of the RAG value chain! All the best !!
What is RAG and how is this different from ChatGPTs (LLMs)?
ChatGPT (or any other variant) is a Large Language Model (LLM) trained on massive amounts of text data (from the internet) and uses deep learning techniques to learn patterns and generate coherent and contextually relevant responses.
However, there are some glaring issues with the LLMs, key ones being — false/out-of-date information on the internet, using non-reliable sources from internet and hallucination. To avoid this, it is always prudent to leverage a reliable data source and extract the output from it.
And that’s where RAG (Retrieval-Augmented Generation) comes into picture. In order to…
