Is your Retrieval Augmented Generation (RAG) system a nightmare of duct tape and broken promises? Maybe it involves chunking strategies you copied and pasted from Stack Overflow (and ChatGPT), embedding models you selected at random (evals , who?), and retrieval that breaks whenever someone adds a new PDF. AutoRAG simplifies the RAG process, eliminating that entire mess by providing a fully managed RAG pipeline that automatically handles ingestion, chunking, embedding, and retrieval. In this session, we build a production-ready knowledge system that continuously monitors your data sources and keeps your AI fresh without requiring manual effort. The live demo includes feeding AutoRAG corporate PDFs, transcripts, and scraped web pages and watching it automatically transform chaos into intelligent search.
Key Takeaways
- Learn to deploy fully managed RAG pipelines that automatically handle data ingestion, chunking, and embedding without custom code.
- Discover how continuous background indexing eliminates manual reprocessing and keeps your AI responses current with changing data.
- Understand cost-effective patterns for scaling semantic search across massive document collections using Cloudflare’s edge infrastructure.