Retrieval Augmented Generation (RAG)

Large Language Models are brilliant but forgetful. We'll show you how we moved beyond simple search to build an RL-powered agent that learns how to find the right information, making AI answers smarter and more accurate.

Traditional RAG systems rely on vector similarity, but often fail on complex documents. Discover PageIndex, a new approach that uses an LLM to reason over a document's structure, leading to more accurate, human-like retrieval.

Forget scattered files and endless searching. We're building a "second brain" where your notes are connected, and an AI agent can navigate them to find answers, uncover insights, and even help you fill in the gaps.

We've been told bigger context windows are the answer, but they often fail. Chroma's new Context-1 is a specialized 20B 'scout' model that proves a smarter, surgical approach to AI retrieval is faster, cheaper, and beats the giants.

Ever wonder why some searches are brilliantly insightful while others are frustratingly literal? We're breaking down the two powerhouses of information retrieval—old-school BM25 and new-school Vector Search—to see how they think, where they shine, and why the best AI systems use them together.