Evaluation & Observability

What Makes LLM Evaluation Hard

Learn why LLM eval is structurally different from traditional ML testing, what the three axes of eval design are, and how to build a mental model for the rest of the track.

Building an Eval Dataset

Learn to treat eval datasets as engineering artifacts: how to seed them, label them, version them, and keep them representative of real production traffic.

Automated Evaluation Methods

Master the spectrum of automated eval techniques, from exact match and string overlap through semantic similarity and LLM-as-judge, and learn which method to apply for which task.

Tracing & Structured Logging

Learn to instrument LLM systems with structured traces that make debugging and performance analysis practical: what to log, how to structure it, and how to avoid PII liability.

Cost Attribution & Token Budgets

Learn to track, attribute, and control LLM API costs before the invoice surprises you: per-request tagging, per-feature aggregation, token budget enforcement, and anomaly alerting.

CI/CD Eval Gates

Learn to build automated eval gates that block deployments when prompt changes, model upgrades, or RAG index updates regress quality: before they reach users.

Production Monitoring & Drift Detection

Learn to detect quality regressions, distribution shifts, and cost anomalies in live LLM systems before users report them: using metrics, statistical process control, and a sample-and-judge pipeline.

Red-teaming & Adversarial Evaluation

Learn to systematically discover failure modes in LLM systems before attackers do: how to run a red-team session, categorize findings, and convert every confirmed vulnerability into a permanent regression test.