Audience
- Product and engineering teams
- Technical profiles building AI products
- Companies working with internal documentation
- Startups that need answers backed by verifiable sources
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Overview
Retrieval-Augmented Generation / RAG Course
How to teach an agent to work with real documents and stop making up answers. A technical course for building RAG systems with documents, sources, and evaluation.
Who it is for
Ideal for
- Companies with technical, legal, commercial, or operational documentation
- Product teams adding AI to existing workflows
- Engineering teams that need judgment for reliable RAG systems
What participants will learn
Design document pipelines, chunking, embeddings, and retrieval
Understand when to use vector databases and how to structure metadata
Create answers with sources, citations, and traceability
Evaluate quality and reduce hallucinations with operational criteria
Modules
Agenda
- 01
Real documents and preparation
Ingestion, cleaning, formats, permissions, and decisions that affect retrieval before the model.
- 02
Embeddings, chunking, and indexing
How to split content, preserve context, and prepare a searchable base.
- 03
Retrieval, prompts, and citations
Patterns to retrieve sources, build context, and generate auditable answers.
- 04
Evaluation and hallucination reduction
How to measure quality, detect failures, and improve the system beyond demos.
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