Engineer what the model sees and returns.
Take one AI workflow from a naive prompt to contracts, structured outputs, budgeted context, grounding, injection defense, routing, human review, and regression gates.
What you will leave with
You can design an AI workflow whose inputs, outputs, evidence, failure behavior, and review boundaries are explicit and testable.
What you will be able to do
The curriculum is organized around these 4 practical outcomes.
Write a prompt contract and output schema
Pack context against a deliberate token budget
Ground answers and defend instruction boundaries
Route work across models, tools, and human review
Course map
Curriculum
Open a module to inspect every section before you start. Your progress follows you through the course.
the context window, tokens and the attention budget, context rot, and more.
prompt contracts, the right altitude, instruction hierarchy, and more.
output schemas, enums and nullables, validation and repair loops, and more.
the token budget ledger, retrieved knowledge, history compaction, and more.
confabulation, grounding and citations, abstention and escalation, and more.
pipelines, not monoliths, routing and cost-quality math, human review gates, and more.
rubrics beyond string checks, the frozen regression set, the release checklist, and more.
Who this course is for
Start with the background you have. The prerequisite notes above tell you exactly what is assumed.
Analysts and operators designing AI workflows
PMs specifying reliable AI behavior
Engineers building prompt and context pipelines
Continue learning
Each course stands on its own. Pick the one closest to the next task you need to handle.
Turn one LLM call into a dependable service.
Build retrieval systems you can measure.
Replace “it feels better” with evidence.
Start the course
Module 1 introduces the language and example used throughout the rest of the course.
