This course covers a practical launch path for LLM features: monitoring, cost control, and safer rollouts.
Includes practical lessons, exercises, and a clear next step after you enroll.
About this course
Curriculum
Lessons are listed in order. Each one has a goal, a walkthrough, practice, and a short check before you continue.
- 01Production is a service, not a notebookSLOs, owners, and a rollback for prompts.
- 02Packaging: API, queues, and timeoutsSync versus async when tools are slow.
- 03Observability for generationsRequest id, token counts, model, prompt version.
- 04Capacity, caching, and degradationWhat users see when the model is down.
- 05Canary a prompt the way you canary codeSmall %, eval, then promote.
- 06On-call for LLM featuresRunbook: spike in cost, spike in errors, bad output cluster.
- 07Workshop: a complete pass on a real taskCombine the earlier lessons into one working example you can keep.
- 08Quality bar, common mistakes, and a 30-day planLock in a checklist so the skill survives after the course ends.
- 09Measure results so you know the work is actually betterAdd a simple evaluation so quality and cost are not guesswork.
- 10Ship it: handoff, monitoring, and the next projectPut the workflow where other people can use it without you in the room.