Turn one LLM call into a dependable service.
Add service promises, traces, traffic controls, cost routing, failure protection, tool permissions, incident response, canaries, and rollback.
What you will leave with
You finish with a production-readiness packet for one LLM service, including its SLO, telemetry, policies, incident controls, and release gate.
What you will be able to do
The curriculum is organized around these 4 practical outcomes.
Define service-level indicators, objectives, and error budgets
Trace requests and engineer latency under load
Control spend, retries, fallbacks, and tool permissions
Run incidents and release with canary and rollback evidence
Course map
Curriculum
Open a module to inspect every section before you start. Your progress follows you through the course.
AskHelpwell workflow, service promises, SLIs and SLOs, and more.
logs, metrics, and traces, span trees, GenAI telemetry, and more.
TTFT and total latency, tail percentiles, queueing and concurrency, and more.
token unit economics, cost per good outcome, prompt caching, and more.
deadline propagation, retry classification, backoff and jitter, and more.
policy enforcement, identity, audience, scope, and tenant, MCP authorization, and more.
symptoms and causes, incident command, containment and rollback, and more.
behavior versioning, shadow traffic, canary releases, and more.
Who this course is for
Start with the background you have. The prerequisite notes above tell you exactly what is assumed.
AI engineers moving from demos to services
Technical leads reviewing production readiness
Platform engineers supporting LLM workloads
Continue learning
Each course stands on its own. Pick the one closest to the next task you need to handle.
Engineer what the model sees and returns.
Replace “it feels better” with evidence.
Build the machinery around an agent loop.
Start the course
Module 1 introduces the language and example used throughout the rest of the course.
