Evaluating and Monitoring LLM Apps
Know whether your AI feature actually works — and catch it when it silently breaks — with evals, tracing, and guardrails.
You built an LLM feature and it demos well. But how do you know it actually works across the inputs real users send, and how do you find out when it silently breaks after a model update or a prompt tweak?
This course is the measurement discipline that answers those questions. You will define what good means for your task, build a golden dataset from real cases, write evals that mean something, gate every change with regression tests, and then trace, monitor, and guardrail the feature in production so quality drops surface as alerts instead of surprises.
The throughline: you cannot improve what you do not measure, vibes are not evaluation, and a human defines what good means.
What you'll be able to do
- Define what good means for your LLM feature and measure it
- Build a golden dataset and task-specific metrics from real cases
- Use LLM-as-judge safely and gate changes with regression tests in CI
- Trace, monitor, and guardrail an LLM app in production and close the loop
Curriculum
Module 1: Why LLM Apps Need Evaluation
FreeThe foundation: why LLM features drift silently, why manual spot-checking fails, how to define good for your task, and how to build a golden dataset from real cases.
Module 2: Building Evals That Mean Something
PremiumTurn your definition of good into evals that hold up: task-specific metrics, reference and rubric scoring, LLM-as-judge with its biases controlled, and regression tests that gate every change.
- Task-specific metrics that mean something15 min
- Reference-based and rubric-based scoring14 min
- LLM-as-judge and how to control its biases16 min
- Regression testing prompt and model changes in CI16 min
Module 3: Monitoring, Tracing, and Guardrails in Production
PremiumKeep the feature working in production: trace and log every request, monitor quality, cost, latency, and failures, add guardrails and fallbacks, and close the loop from production back into your evals.
- Tracing and logging every request14 min
- Online metrics: quality, cost, latency, and failures15 min
- Guardrails and fallbacks in production15 min
- Human review and closing the loop16 min
Get the full course.
Start Module 1 free today. More modules coming soon.
Want all 71? Get the All-Access Bundle for $99 — one purchase, every course.