Build Your Own AI-Powered Document Search

PLEASE NOTE: This is a pilot event with limited seats available. The goal is to hold this event again. If you don't get a seat and would like to be notified when this event is held in the future, please join the waiting list.

Large language models (LLMs) are powerful, but they hallucinate, confidently generating plausible-sounding information that isn't true. They also can't access your private documents, internal knowledge bases, or recent information outside their training data.

Retrieval-Augmented Generation (RAG) solves this by giving LLMs a "context window steering lever." Instead of relying solely on trained knowledge, RAG retrieves relevant chunks from your own corpus and feeds them to the model at query time, grounding responses in actual source material.

In this hands-on workshop, you'll build a working RAG system from scratch. You'll ingest documents, generate embeddings, store them in a vector database, write similarity queries, and wire everything into…