Introduction
Large language models (LLMs) can generate fluent and useful responses, but their training data doesn't contain every piece of information that an application might need. A model might not know about your organization's private documents, and its training data can become outdated. It can also produce a plausible response that isn't supported by reliable evidence.
Retrieval-augmented generation (RAG) addresses these limitations by combining search with generative AI. Before asking a model to answer a question, a RAG application finds relevant information and adds it to the prompt. The model can then use that information to produce a more relevant, grounded response.
In this module, you'll explore the main stages of RAG and the key decisions that influence the quality of a RAG solution.
Note
We recognize that different people like to learn in different ways. You can choose to complete this module in video-based format or you can read the content as text and images. The text contains greater detail than the videos, so in some cases you might want to refer to it as supplemental material to the video presentation.