Building Smarter LLMs with LangChain and RAG: A Beginner’s Guide

Author(s): Harshit Kandoi

Originally published on Towards AI.

Photo by Alberto Moya on Unsplash

Ever tried your hand at an LLM’s question and got a confident, slick solution that turned out to be completely wrong? I have — many times. I remember asking, “Can I fine-tune LLM on my laptop?” and getting a curious “Yes!” Only to find out, twenty browser tabs and a mini breakdown later, that it’s not possible until your laptop is secretly a supercomputer”.

That moment right there? I started digging into ways to make language models smarter and more grounded in reality. LLMs are amazing, but they have a serious drawback that they can easily hallucinate. They make stuff up. They don’t recognize what they don’t know. And for humans like us, students, junior developers, or just AI enthusiasts, it is both fascinating and frustrating.

That’s when I came across LangChain and RAG, two tools that seemed complicated but turned out to be absolute game-changers once I tried them. They gave me a way to “feed” real documents into the LLM models and get responses that are now no longer simply coherent, however accurate. No more AI fantasy novels pretending to be facts.

In this blog, I want to share my learnings with you. If you’re just… 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