How to Build a RAG Application?

AI advancements are happening at a rapid pace, and one fascinating concept that’s gaining traction is the Retrieval-Augmented Generation (RAG). As part of an internship assignment, I developed a RAG app with a JSON file as the knowledge corpus. I am now writing this article to walk you through the process.

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Screenshot of the app (Source: GitHub)

Let’s dive in and start building your own RAG-powered app!

All the code used in this tutorial, and more can be found here: GitHub.

Everyone is talking about RAG, but what is it actually and why do we need them?

Popular large language models (LLMs) like ChatGPT, Claude, and Llama excel at general-purpose tasks. However, they often fall short when asked to handle domain-specific queries. For example, if you need detailed insights or answers from a specialized dataset, these models may provide only generic responses.

To tackle this limitation, there are a few approaches:

1. Training Your Own LLM: Building an entirely new model from scratch for your use case. This is highly resource-intensive.
2. Fine-Tuning Pretrained LLMs: Tailoring an existing model to your specific dataset, which is less costly but still…