Build ChatGPT... but for your own data. Learn how to ingest documents, retrieve relevant context, and generate grounded answers from your content.
Retrieval-Augmented Generation (RAG) is how modern LLMs "know" your data. This playbook shows you how to ingest docs, semantically search them, and return grounded answers using an LLM. Perfect for teams building support bots, internal search tools, or AI assistants that need to be factual, verifiable, and secure.
Follow this step-by-step guide to build your RAG system
Learn how RAG connects your LLM with real, private, and up-to-date data — the right way.
Start ChapterProcess PDFs, markdown, and HTML into smart chunks ready for vector search and fast retrieval.
Start ChapterCombine semantic search with LLM generation to build an app that answers using your data.
Start ChapterHost your RAG pipeline with confidence — handle auth, load, cost, and privacy at scale.
Start Chapter