Build RAG from Scratch

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?

JavaScript

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.

Phil Nash

Phil is a developer relations engineer for DataStax and Google Developer Expert living in Melbourne, Australia. He loves working with JavaScript, TypeScript or Ruby to build web applications and tools to help developers. He once helped build a website that captured the world's favourite sandwich fillings. He has too many GitHub repositories.

Away from the keyboard, Phil listens to ska punk, hangs out with his miniature dachshund (also called Ruby), and is on a mission to discover the world's best beers.

Phil tweets at @philnash and you can find him elsewhere online at https://philna.sh.

NDC { London }

Build RAG from Scratch

Phil Nash