AI Engineering: Retrieval Augmented Generation (RAG) for LLMs

Source: https://zerotomastery.io/courses/inside-react/

What Is Retrieval Augmented Generation (RAG)?

Have you ever wondered why some AI systems can feel limited, giving answers that seem a bit generic or off-target? Well that’s because they’re limited to the knowledge in their training data.

And that’s a lot of data. But it’s not everything. It doesn’t include private data, nor does it include recent data that has been created since the model was trained.

Retrieval-Augmented Generation, or RAG, addresses this by supplementing AI models with that private or new information.

Instead of relying only on what it was trained on, RAG retrieves up-to-date, relevant information from a database or document.

Here’s how it works: the system first finds the most relevant pieces of information for a given question (retrieval). Then, it uses a language model to generate a response based on that information (generation). That’s why it’s called retrieval-augmented generation!

The result is an AI that combines the best of both worlds: real-time access to external knowledge and the ability to express it clearly.