In our third Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation.

RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases.

In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search.

Follow-along live, thanks to GitHub Models and GitHub Codespaces.
If you'd like to follow along with the live examples, make sure you've got a GitHub account.

You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord.

Check out more resources here