Follow the LLM Evaluation & Observability learning path on AI Builders Network - a free, structured roadmap with curated resources and step-by-step progress tracking.
This path is for engineers who own AI quality in production: the people asked 'is it actually working?' after launch. Evaluation is the single most under-supplied skill in AI teams: the MIT NANDA finding that 95% of enterprise AI pilots fail traces overwhelmingly to missing quality measurement.
By the end you'll be able to: Build golden datasets from real traffic, including adversarial cases; Run calibrated LLM-as-judge pipelines with measured human agreement; Gate CI on behavioral evals so regressions block merge; Trace production LLM calls with cost, latency, and drift alerts; Red-team your own system and convert findings into regression tests.
Evals are systematic tests for AI behavior: a dataset of real inputs, a definition of good output for each, and automated scoring, exact-match, rubric, or model-graded. They serve the role unit tests serve in classical software, catching quality regressions when prompts, models, or retrieval change.
Yes, LLM-as-judge is standard practice for scaling evaluation, but judges have measurable biases toward position, verbosity, and their own outputs. Calibrate the judge against human labels until agreement is quantified, and re-calibrate whenever you change the judge model. An uncalibrated judge produces confident noise.
Offline evaluation scores a fixed golden dataset before deploy: it gates changes. Online evaluation samples live production traffic for scoring and drift alerts: it catches what your dataset missed. Mature teams close the loop: flagged production failures become new golden dataset entries.