Were you wondering how Large Language Models power Q&A systems? How does Retrieval Augmented Generation (RAG) really work? If I have proprietary data, can I build a chatbot that helps me find the answers in my documents? You might find the answer in this overview on RAG applications powered by LLMs on proprietary data.
If you want to learn about design considerations and best practices for building a RAG application you'll find MLOps.community's course on Introduction to Q&A systems with LLMs very relevant. We've tried to keep the course vendor agnostic so that you can learn the real workings of the system without being sold on a particular tool. And most importantly code examples in our optional labs to help you understand the underlying mechanisms of RAG systems! Course subscribers also get access to our Slack channel to learn and share.