Retrieval augmented generation (RAG) provides large language models with up to date information and helps them hallucinate less. But how does it all work beneath the covers?

In this live coding session we'll build the components of a RAG system from scratch. (Aside from the LLM, there probably isn't time for that!) By building our own, we'll understand vectorisation, similarity search, and the role of embedding models and vector databases. We'll then plug it all together to see our augmented bot in action.

You'll get a good grounding in the components of successful chatbots and why they work.