Overview on RAG

Imagine chatting with a bot that doesn’t just guess answers based on what it was trained on months ago — but actually reads and understands the latest documents, help articles, or product manuals in real-time before answering you.

As we all know that Large Language Models (LLMs) like ChatGPT are great at answering questions, but they can sometimes give wrong or outdated answers because their knowledge is fixed from training.

In order to train the model with latest dataset we have to fine-tune the model. In other words, fine-tuning modifies the model’s weights by training it on your specific dataset. But we need resources to train the model which is time-consuming and sometime expensive to do it.

That's when RAG comes into picture! RAG, is short for Retrieval-Augmented Generation, that use – large language models (LLMs) and real-time information retrieval.

Instead of relying solely on what the model “knows,” a RAG-based chatbot fetches the most relevant knowledge from your content — and then crafts smart, contextual responses.

That’s the magic of a RAG-powered chatbot.

In this blog, I’ll walk you through how RAG works, why it’s a game-changer for support bots, internal tools, or any domain-specific assistant — and how you can build one by leveraging GitHub repository.

Let’s dive in! 🔍🤖