04Learning path
Test, observe, and improve AI systems in production with evaluation suites, tracing, and monitoring.
01
Shipping an LLM feature is the start of the work, not the end. Quality can drift as inputs change, a prompt tweak can cause silent regressions, and costs can grow faster than usage. This path focuses on the practices that keep LLM systems dependable after launch.
You will build evaluation suites that combine deterministic checks with carefully validated model-graded scoring, and wire them into CI. Then you will instrument a running service with OpenTelemetry, control latency and cost, and set up monitoring that feeds production failures back into your test sets.
The result is a repeatable loop — evaluate, deploy, observe, analyze, improve — that you can apply to any LLM application you work on.
Who it’s forEngineers responsible for the quality, reliability, and cost of LLM applications already in use or about to launch.
02
03
BuildAssemble a labeled test set of at least 50 cases for an existing LLM feature.
MilestoneYour test set is versioned in the repository with written labeling guidelines.
BuildBuild a scorer library and use it to produce a categorized failure report.
MilestoneYou know your system's three most common failure types and how often each occurs.
BuildAdd the evaluation suite to CI with pass/fail thresholds.
MilestoneA prompt change that lowers quality below threshold fails the build.
BuildInstrument a FastAPI service and add a caching layer with measured impact.
MilestoneYou can trace any request end to end and report its latency and cost.
BuildSet up alerts for latency, error rate, and cost, and a job that samples traffic for review.
MilestoneProduction failures flow back into your test set on a regular cadence.
05
Build a repeatable evaluation harness that runs test cases against prompts and models, scores the results and tracks regressions between versions.
Instrument an LLM application with tracing, then build dashboards and alerts for cost, latency, errors and output quality signals.
06
Outcomes describe skills and portfolio artifacts. No employment outcome is promised.
Build the foundations, projects, and habits needed to design more dependable AI applications.