RAG Architecture: Production-Grade Retrieval Systems

About this course

This course contains the use of artificial intelligence.

Large language models fail on enterprise knowledge: they hallucinate, go stale at their training cutoff, and ignore access control. This course teaches you to build production-grade retrieval-augmented generation (RAG) systems that answer from your organization's documents — with citations, permissions, measurable quality, and predictable cost.

Across 13 sections and 53 lessons (9 hours of video), you will design the complete RAG pipeline: document ingestion and preprocessing, chunking strategies, embeddings and vector representations, vector databases and indexing, hybrid search across dense, sparse, and reranking, prompt engineering for grounded generation, and rigorous evaluation of faithfulness, relevance, and recall.

Then you take it to production: latency budgets, cost models, scaling patterns, capacity planning, and load testing — plus the parts most courses skip: security, governance, and compliance. You will implement access control that survives retrieval, defend against prompt injection and data exfiltration, handle retention, deletion, and auditability, and run a red-team testing playbook.

Everything is demonstrated in working Python code shown on screen, including three full live-demo lessons, and the capstone has you design a complete production RAG system with a production-readiness review. You will learn to choose chunking, embedding, vector-database, and indexing strategies from evidence rather than hype, and to measure what matters: faithfulness, relevance, recall, latency, and cost per successful answer.

Built for software engineers, AI/ML architects, platform engineers, and technology leaders shipping AI features over enterprise knowledge. Follows the book RAG Architecture: Designing Production-Grade Retrieval Systems.

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