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.

Cost Management

LLM costs are non-linear and easy to underestimate — especially in multi-agent systems where one orchestration call spawns dozens of sub-calls. This module covers token economics, prompt caching, cost ceilings with graceful degradation, and the attribution infrastructure needed to run LLM workloads sustainably.

Evaluating the Evaluator

Your eval suite is only useful if it tracks what 'good' actually means — and that definition shifts as your product evolves. This module covers the meta-loop most teams skip: validating that your judges, metrics, and test cases remain calibrated to real quality, not just to themselves.

Multimodal Evaluation & Observability

Text evals don't transfer. When your pipeline processes images, audio, or video, each modality introduces failure modes that a text judge cannot see. This module covers ground-truth dataset design, judge strategies, and observability instrumentation for non-text pipelines.

Human Feedback Operations

Human review of AI output is not a checkbox — it's an operational discipline with its own failure modes. Reviewer quality degrades over time, labels drift, and retraining on degraded data makes models worse. This module covers the workflows, tooling, and quality controls that keep human feedback reliable.