This is a quick intro post to RAG, but I plan to dive way deeper in a future post(s).

We’ve all seen how large language models like GPT-4 can generate human-like text, answer complex questions, and even write code. It’s pretty impressive stuff. But here’s the thing - they have some serious limitations. They sometimes make stuff up (you know, that “hallucination” problem everyone talks about), and they can’t access information beyond when they were last trained.

This is exactly where RAG comes in to save the day.

So What Exactly is RAG?

Think of it like this: an LLM is like a brilliant student who read every book in the library up until 2022. RAG hands that student a library card and says “go look up the latest info before you answer.”

Instead of just relying on what it learned during training, RAG works in two simple steps:

  1. First, it looks stuff up: When you ask a question, the system first searches through relevant documents, articles, or data from an external knowledge base. This could be anything from company documents to the entire internet.

  2. Then it answers: Once it finds the relevant information, it combines that with your original question and passes everything to the language model. Now the model has the current, accurate info right there and can give you a much better answer.

Why Should You Care About RAG?

This two-step approach is changing the game for AI applications:

  • Way more accurate: By giving the model verified information, it’s way less likely to make stuff up. This is huge for business applications where accuracy matters.

  • Always up-to-date: LLMs are stuck in the past, but RAG lets them access current information. Perfect for things like news summaries or product support.

  • Shows its work: One of the coolest things about RAG is that it can often tell you where it found the information. You can actually check its sources.

  • No expensive retraining: Want an AI that knows your company’s products? Instead of retraining the whole model (which costs a fortune), you just connect it to your company documents.

RAG in the Wild

This isn’t just theory: companies are using RAG right now to build some pretty impressive stuff. Customer service bots that can pull from a company’s entire knowledge base, medical research tools that synthesize the latest studies, you name it.

RAG is basically taking the creative power of LLMs and grounding it in reality. Making them more factual, more current, and honestly, way more useful. This is an important concept to understand if you want to break into an AI related role by the way!