Context and Grounding — Free AI Learning Track

How to make models answer from trusted sources.

The Context and Grounding track answers one question: how do you make a model answer from trusted sources instead of its own memory? A base model knows only its training data, and most production failures trace back to the model missing information it was never given.

Three modules cover the discipline. How RAG Works explains retrieval-augmented generation end to end, from chunking and embeddings through hybrid search, re-ranking, and citation, plus the division of labor between retrieval and fine-tuning. Context Engineering 101 treats the context window as a scarce resource and teaches the techniques that keep long-running work coherent: compaction, structured note-taking, sub-agents, and progressive disclosure. Context Engineering vs Prompt Engineering draws the line between wording a request and architecting the information a model receives, through the four pillars of memory, retrieval, state, and tool access.

Together, the modules shift your attention from how a prompt is phrased to what the model can actually see, which is where reliable systems are won.

Modules in this track

• How RAG Works — Retrieval-augmented generation is the standard pattern for grounding AI answers in trusted source material.
• Context Engineering 101 — The hardest problem in production AI is not phrasing the question. It is deciding what information the model should see at each step.
• Context Engineering vs Prompt Engineering — A failed AI feature is almost always an architecture failure, not a wording failure.

Part of the free AI Learning Hub by Shahzad Asghar.