Most RAG content online explains what retrieval-augmented generation is. This course teaches you to build it: from raw documents to a monitored production system, across 45 lessons and 12 sections.
A note before you enroll: this is the one advanced technical course in our catalog. Every other course we publish requires no coding. This one goes deeper: chunking algorithms, embedding models, vector databases, retrieval pipelines, and production operations. You do not need to be a software engineer, but you should be comfortable with technical material and ready to work hands-on.
Who this course is for
- Engineers and developers building RAG systems for their company or clients
- Technical marketers and consultants who need to specify, buy, or audit RAG work credibly
- AI practitioners moving from prompting to building grounded systems
- Anyone who has watched a RAG demo impress the room and then fail on real data
Why this course exists
Industry analyses converge on a sobering pattern: the large majority of enterprise RAG projects fail in production, and almost never because of the AI model. They fail on data quality, chunking decisions, retrieval strategy, evaluation, and operations. Those five things are this course.
Every major chapter opens with a documented real-world case, cited to its source: the lawyer sanctioned over ChatGPT’s fabricated case law, the Stanford research that changed how practitioners construct context, the peer-reviewed study of seven recurring RAG failure points from real deployments. No invented anecdotes. Where an example is illustrative rather than documented, it is labeled as such.
What you will be able to do
- Ingest and clean any document type: PDFs, Office files, HTML, spreadsheets, images, audio, code
- Choose and implement the right chunking strategy, and know when the sophisticated option is not worth it
- Select embedding models on evidence: benchmarks, cost, and domain fit
- Design vector database architecture: indexing, metadata, filtering, multi-tenancy
- Build retrieval that actually retrieves: hybrid search, reranking, query transformation, agentic retrieval
- Engineer generation that stays grounded: context management, citation, handling “I don’t know”
- Evaluate systematically with retrieval and generation metrics, and debug what the metrics surface
- Take a system to production: scaling, caching, monitoring, security, and access control
- Recognize and apply advanced architectures: Corrective RAG, Self-RAG, Graph RAG, multi-modal and agentic RAG
Course structure
45 lessons across 12 sections: Foundations · Data Ingestion & Document Processing · Chunking · Embeddings · Vector Stores & Indexing · Retrieval Strategies · Generation & Synthesis · Advanced RAG Architectures · Evaluation & Testing · Production & Operations · Practical Projects & Case Studies · Appendices (tool reference, embedding model comparison tables, vector database feature matrix, prompt template collection, troubleshooting guide, glossary).
Approximately 25+ hours of material. Self-paced, lifetime access, updated as the field moves.
Prerequisites
Comfort with technical concepts and a working familiarity with AI language models. Programming experience helps for the hands-on projects but is not required to complete the course; every concept is explained before it is used.
