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What You'll Learn

Master agentic RAG patterns — ReAct
Self-RAG
Corrective RAG
and Reflexion — for building self-correcting retrieval systems,Implement advanced retrieval architectures including GraphRAG
RAG-Fusion
contextual retrieval
late chunking
and multi-agent RAG,Design multi-modal
multi-source
and structured RAG systems that combine vision-language models
SQL
and federated retrieval,Apply enterprise-grade security
governance
and cost/scaling practices to deploy production RAG systems with confidence

Requirements

This is an advanced course — you should already be comfortable with core RAG concepts (retrieval
embeddings
vector search
chunking
and basic system architecture) before starting
ideally through hands-on experience or a foundational RAG course. Familiarity with LLM APIs and basic Python is helpful for following the technical scenarios
though you wont be writing code during this course. If youre new to RAG entirely
we recommend starting with a fundamentals-level RAG course first.

Description

You already know the RAG basics — retrieval, embeddings, chunking, generation. This course takes you into the territory that separates a working prototype from a production-grade, enterprise-ready RAG system.

Through 600 scenario-based practice questions across six comprehensive tests, you'll build the judgment to know not just what advanced RAG techniques exist, but when each one actually earns its complexity.

You'll cover:

Agentic & Self-Correcting RAG Patterns — ReAct, Self-RAG, Corrective RAG (CRAG), Reflexion, and adaptive retrieval strategies
Knowledge-Graph & Structured RAG — GraphRAG, RAPTOR, and RAG over structured, tabular, and SQL data
Multi-Modal & Multi-Source RAG — vision-language RAG, federated retrieval, temporal RAG, and audio/video content
Advanced Retrieval Architectures — RAG-Fusion, contextual retrieval, late chunking, speculative RAG, and multi-agent retrieval
Enterprise Security, Governance & Compliance — access control, PII handling, regulatory frameworks, auditability, and incident response
Advanced Scaling, Cost & Production Optimization — vector database scaling, inference cost optimization, capacity planning, and multi-region deployment

Every question comes with a full explanation, reinforcing a consistent theme throughout: sophisticated techniques are valuable exactly to the extent they solve real, evidenced problems — never simply because they're advanced. Whether you're architecting a new RAG system, hardening an existing one for production, or preparing for senior technical interviews in this space, this course gives you the systematic, evidence-based framework to make sound architectural decisions rather than chasing trends.

Who this course is for:

This course is for engineers and AI practitioners who already understand RAG basics and want to go deeper — into agentic patterns
advanced retrieval architectures
knowledge graphs
multi-modal systems
and the enterprise realities of running RAG in production (security
governance
cost
and scale). Its built for people whove moved past tutorial-level RAG and are now facing real architectural tradeoffs: when does an agentic pattern actually earn its complexity
how do you scale a vector database past millions of documents
what does genuine enterprise governance for RAG look like. If youre architecting or maintaining a production RAG system and want a systematic
evidence-based way to evaluate advanced techniques rather than chasing every new trend
this course is for you.