RAG & LangGraph Agents over Company Documents

About this course

This course contains the use of artificial intelligence.

THE PROBLEMAlmost anyone can build a RAG demo in an afternoon: load a few clean PDFs, split them into chunks, embed them, and ask questions. It works — until it meets a real company corpus. Real documents are messy: scanned PDFs, Office files with tables, wikis, tickets, duplicates, outdated versions and documents that only some people are allowed to see. In production, naive RAG fails in predictable ways: stale indexes, bad chunks, answers that cite nothing, retrieval that leaks restricted documents, silent quality decay, and costs nobody budgeted for. The demo gap is not a model problem. It is a systems problem.

THE PROPOSALRAG & LangGraph Agents over Company Documents is a complete, code-first course for developers who need to build retrieval systems that survive production. It follows The Production RAG Method across 7 modules and 30 lessons: from ingestion and parsing, through chunking, embeddings and vector stores, to hybrid search, reranking, evaluation, LangGraph agents, security, deployment and monitoring. Every stage is taught as an engineering decision — what to choose, what it costs, how to measure it, and how it fails. The course includes a complete Python code pack (LangChain/LangGraph, Qdrant, FastAPI) so you build alongside the lessons instead of only watching slides. The central rule, repeated throughout the course: retrieval must respect permissions, answers must be grounded in cited documents, and quality must be measured — never assumed.

MODULE BY MODULE

Module 1 — Foundations: the map of production RAG over company documents, why naive RAG fails in production (the demo gap), the anatomy of a RAG system in seven stages end to end, and why company documents are different from clean demo corpora.

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