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Keep shoppingAdvanced RAG Techniques: Enterprise Best Practices
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114 students
Advanced RAG Techniques: Enterprise Best Practices
Enterprise RAG: chunking, hybrid search, re-ranking, query enhancement, Self-RAG, CRAG, Graph RAG, eval & governance
Created byDr. Amar Massoud
Last updated 7/2026
English
What you'll learn
- Design an advanced RAG reference architecture that goes well beyond naive retrieve-and-generate
- Implement semantic, document-aware, and contextual chunking and measure their impact on retrieval quality
- Build hybrid search with BM25, dense vectors, Reciprocal Rank Fusion, and cross-encoder re-ranking
- Apply query enhancement techniques: rewriting, decomposition, multi-query fan-out, and HyDE
- Engineer self-correcting patterns including Self-RAG, Corrective RAG (CRAG), and Graph RAG
- Perform multimodal retrieval over tables, images, and diagrams in real documents
- Build an automated evaluation pipeline measuring faithfulness and context recall/precision
- Optimize cost-per-query, add observability and tracing, and enforce PHI/PII data governance
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Course content
7 sections • 38 lectures • 3h 27m total length
Requirements
- Working knowledge of Python (functions, classes, virtual environments)
- Basic familiarity with RAG or LLM apps: embeddings and vector databases
Description
This course contains the use of artificial intelligence.
Most RAG tutorials stop at "embed some text, drop it in a vector database, and call an LLM." That naive pipeline falls apart the moment it meets a real enterprise corpus: dense tables, scanned figures, exact drug codes, ambiguous questions, and answers that absolutely must be faithful and auditable.
This course is the missing engineering playbook for advanced, production-grade Retrieval-Augmented Generation. You will rebuild a realistic system end to end: HelixRAG, a clinical knowledge assistant for a 4,000-employee health system, where every answer must be grounded, cited, measured, and governed before it can ship. Working hands-on against a sensitive, multimodal corpus, you will move the numbers that matter, faithfulness, context recall, cost per query, and latency.
What you will master: advanced chunking and indexing (semantic, document-aware, contextual retrieval, small-to-big); hybrid search that fuses BM25 and dense vectors with Reciprocal Rank Fusion, then sharpens results with cross-encoder re-ranking; query enhancement including rewriting, decomposition, multi-query fan-out, and HyDE; self-correcting and agentic patterns such as Self-RAG, Corrective RAG (CRAG), and Graph RAG; multimodal retrieval over tables, images, and diagrams; and production hardening with automated evaluation pipelines, cost-per-query optimization, observability and tracing, and PHI/PII data governance for HIPAA and GDPR.
Every technique is taught the way a senior engineer would judge it: by measured retrieval and answer quality, cost, and risk, not toy demos. You will finish with a best-practice checklist and a production-grade reference architecture you can adapt to your own domain.
If you are an AI engineer, ML engineer, or developer ready to move beyond naive RAG, this course gives you the patterns, trade-offs, and evaluation discipline to build retrieval systems you can trust in production.
Who this course is for:
- AI and ML engineers building production retrieval systems with LLMs
- Software developers integrating LLMs into real applications
- Technical leads and architects evaluating advanced RAG for the enterprise