Advanced RAG & Knowledge

Production RAG for Enterprise Knowledge Systems

A hands-on engineering course on retrieval-augmented generation as it is actually shipped in 2025-26: layout-aware parsing, contextual retrieval, hybrid search with reranking, agentic RAG, MCP-based federation, permission-aware retrieval, injection defense, evaluation suites, and cost and latency engineering. Written for teams putting company knowledge behind an LLM, not for demo notebooks.

25 Modules
4.0h Duration
Text + Code Format
Free Cost
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Prerequisites
  • Comfortable Python (async, type hints, HTTP clients)
  • Basic familiarity with LLM APIs and embeddings
  • Working knowledge of SQL and at least one database

What you'll learn

  • Decide when RAG beats long context, fine-tuning, or live agentic search, and when to combine them
  • Build ingestion pipelines with layout-aware parsing, VLM OCR, and incremental sync that keeps indexes fresh
  • Apply 2025-26 chunking practice: structure-aware splitting, contextual retrieval, and late chunking
  • Engineer two-stage retrieval: hybrid dense plus lexical search fused with RRF, then cross-encoder reranking
  • Design query pipelines with rewriting, decomposition, metadata filtering, and GraphRAG for relationship questions
  • Ship grounded generation with span-level citations, agentic retrieval loops, and MCP-federated sources
  • Enforce document-level permissions at query time and defend against indirect prompt injection
  • Stand up evaluation suites, caching layers, tracing, and drift monitoring for the running system
  • Design and present a complete production RAG platform end to end in the capstone project

Course Curriculum

25 modules · 4.0 hours total

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