LLM Observability and Evaluation
Tracing agent steps, cost and latency dashboards, offline evals vs online monitoring, LLM-as-judge, CI regression suites, and canarying prompt changes
Interview-ready coverage of running LLM systems in production: what a full request trace must capture, token/cost/latency dashboards as product metrics, the precise line between offline evals and online monitoring, LLM-as-judge as a general technique with its biases and calibration, human review workflows, CI regression suites that treat prompts and models as code, canarying prompt and model changes, and a worked dashboard-diagnosis example.
Practice questions (5)
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Diagnosing a Two-Week Quality Regression from a Dashboard
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Designing a CI Regression Gate for Prompt and Model Changes
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Calibrating an LLM-as-Judge and Defending Against Its Biases
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Designing a Trace Schema for a Tool-Using Agent
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Canarying a Prompt Change with Rollback Criteria
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