Skill guide
Retrieval-augmented generation (RAG)
RAG retrieves relevant source material at request time and gives it to a generative model so an answer can be grounded in selected evidence.
Why this capability matters
It is useful when answers must reflect private or frequently changing material rather than model memory alone.
What competent practice includes
Document preparation and chunking
Retrieval design
Context assembly
Citation and answer controls
Faithfulness evaluation
Portfolio evidence
A grounded knowledge assistant over a safe document set, with citations, access assumptions, retrieval tests, and known limitations.
See public project briefsProfessional applications
These are fields of application, not guaranteed job or income outcomes.
Policy assistantsResearch librariesProduct support knowledgeSOP searchDocument analysis
Courses that develop this skill
AI Engineering
Generative AI (GenAI)
Foundational to advanced generative AI — how these models work and how to build real applications on top of them.
Agentic Systems
Agentic AI Development with LangChain & LangGraph
The production framework stack for building controllable, stateful multi-agent applications.
Data & ML
Data Science with Generative AI
Where classic data science meets generative models — RAG, fine-tuning, and LLM-powered analytics.
Connect the skill to a market path
Learn how this capability fits inside a complete problem, proof, service, and delivery journey.