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AIintermediate
📈LLM, agent, and RAG evaluation & monitoring
A compact track on how to know your LLM-powered system is actually working: LLM benchmarks vs. real evals, agent trajectory evaluation, RAG evaluation in production, and the OpenTelemetry GenAI conventions that tie traces across LangSmith, Phoenix, and Datadog LLM.
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Lessons in order
- 1AILLM evaluation: how to know your model output is actually goodStart
- 2AIInterpreting LLM Benchmarks: What MMLU, GPQA, and SWE-bench Actually MeasureStart
- 3ProgrammingLangSmith: Tracing & Evaluating Your LLM ApplicationsStart
- 4AIEvaluating AI Agents: From Final Answers to Full TrajectoriesStart
- 5AIRAG Evaluation in Production: Metrics, Tools, and CadenceStart
- 6AILLM Observability with OpenTelemetry: GenAI Semantic ConventionsStart