Track 2
Context and Grounding
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 (3)
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.
How this track fits
The AI Learning Hub is built as a sequence rather than a library. Foundations covers what the models are and how they behave. Context covers the material a model needs around it before its answers can be trusted. Practice covers what changes when a system leaves a demonstration and meets real users, real data, and a real obligation to be right.
Tracks can be read in any order, but the sequence matters more than it looks. Most failures in institutional AI are not model failures. They come from deploying a capable model into an environment that was never prepared for it: no owner, no stopping condition, no measure agreed in advance. The later tracks exist because the earlier ones are not sufficient on their own.
For the foundations underneath all of this, the free Learn LLMs course works from next-token prediction up to the Transformer, and LLM Architecture Explained covers the same ground visually. To move from models to systems that act, see Learn Agentic AI. For the governance that has to sit around any of it, see AI governance in the United Nations and the governance templates.