Introduction to Retrieval Augmented Generation (RAG)

Key Takeaways

every organization is unique and that's why in many cases you'll need custom agents but that doesn't mean you have to build from scratch okay but how can I really customize my agents when I'm using pre-existing geni models is just adding some tooling and reasoning Loop logic enough well kind of that will already start to make a huge difference there's one type of tooling that we haven't discussed as deeply yet that made a huge difference in the world of generative AI are you talking about rag retrieval augmented generation sure am before rag gen models were static knowledge bases meaning their knowledge was limited to what was available in the training data yeah and once trained the knowledge base didn't dynamically update with new information and needed additional training or fine-tuning meaning you could get outdated or relevant results or results that weren't quite specific to your organization right and even though preag agents might have used tools they weren't using those tools to process and learn from the data the tools provided then came rag creating a system where the model can retrieve and learn from data provided by tools got it so if you want solutions that truly make a difference you want your agent to be processing and learning from your company's data yeah so in this lesson we go through how agents with rag systems use tooling and what types of tooling they use ready to learn let's Dive In