AI is a hot topic right now—and for good reason! Natural Language Search and Retrieval Augmented Generation (RAG) are powerful ways to leverage the data already stored in Postgres. Why settle for basic Full Text Search when you can search for intent and related concepts?

But let’s be honest—actually implementing it is a huge pain. You have to pick an embedding model, generate and maintain vector indexes, handle similarity searches, manage a large language model API, juggle prompts and references... it gets complex fast.

Or—you can just use the pg_vectorize extension.

Join this on-demand session to see how you can build a simple, self-maintaining RAG application using just a few PostgreSQL queries. We’ll break down the AI theory behind it, show you how Postgres fits into the modern AI stack (thanks to pgvector and friends), and most importantly—demystify AI so anyone can use it.

Whether you're a seasoned developer or just AI-curious, this session will show you how to get started with real-world AI in Postgres—fast.