The Simple Principle Behind Retrieval Augmented Generation in Large Language Models

Author(s): Krupesh Raikar

Originally published on Towards AI.

Understand RAG intuitively and implement a chat pipeline with your documents using LangChain and Llamma v2
Photo by Mika Baumeister on Unsplash

In a timeframe that can only be best described as a blink of an eye, large language models have exploded in the general public consciousness.

Even if you have nothing to do with tech, you (and your grandma) have heard of ChatGPT!

It’s easy, accessible, and mighty — even on the free tier.

ChatGPT is pretty good at tackling general questions like:

What is the speed of a rock in free fall from a height of 10 meters?

But what if you want it to calculate something proprietary, like:

What is the exact trajectory of landing a SpaceX rocket?

In the first case, it gets the answer correct (14 m/s in case you were wondering), but in the second case…

IT FAILS.

Were you a SpaceX employee, you wouldn’t want to risk inputting your proprietary data into an external LLM — that could be a big security risk for the organization!

What do you do in such a case where proprietary documents are involved?Wouldn’t it be wonderful if you could pose questions to your documents too?

Well, one of the methods is to train an LLM with your data.

If you wish to do that, I hope you have a billion dollars in the bank, or you… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI