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.
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
AskHelpwell, a self-contained support workflow followed from one ticket through a controlled production release.
Basic familiarity with API requests and LLM applications. No prior LDS course or operations background is required.
Open any module to review its exact sections. Progress and completion follow you through the course.
Topics include AskHelpwell workflow, service promises, SLIs and SLOs, and more.
Topics include logs, metrics, and traces, span trees, GenAI telemetry, and more.
Topics include TTFT and total latency, tail percentiles, queueing and concurrency, and more.
Topics include token unit economics, cost per good outcome, prompt caching, and more.
Topics include deadline propagation, retry classification, backoff and jitter, and more.
Topics include policy enforcement, identity, audience, scope, and tenant, MCP authorization, and more.
Topics include symptoms and causes, incident command, containment and rollback, and more.
Topics include behavior versioning, shadow traffic, canary releases, and more.
AI engineers moving from demos to services
Technical leads reviewing production readiness
Platform engineers supporting LLM workloads
These are independent LDS courses that extend the closest skills. Choose the direction that matches what you want to build next.
Engineer what the model sees and returns.
Replace “it feels better” with evidence.
Build the machinery around an agent loop.
The first module establishes the language and example used throughout the rest of the course.
