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.
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