Prompt and Context Engineering

Anyone can coax one impressive answer out of a language model. Getting dependable answers — on messy real inputs, at scale, when a wrong one actually costs something — is a different skill, and hardly anyone is taught it. That skill is what this course is about. You'll learn to build a prompt from its real components rather than borrowed incantations, to engineer the context a model needs around it — the right documents, examples, tools, and memory, and nothing that just adds noise — and then to do the thing most people skip entirely: measure. Golden datasets, deterministic checks, an LLM-as-judge calibrated against human labels, regression suites, metrics that actually fit RAG and agent systems. The loop that tells you whether a change made things better or just different. It's grounded in how serious practitioners and the major model providers work today, not folklore that breaks with the next release. Code-shaped artifacts show up here — a schema, a tool definition, an assertion — but you won't write any, and technical and non-technical learners follow the same path. Across thirty-six lessons you'll go from how models behave to a complete, repeatable engineering loop, with the safety and prompt-injection know-how any real system needs along the way. Stop shipping demos that work once. Start building things that hold.