Understanding model benchmarks, LLM application evaluation, and tooling.

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Part 10 of an LLMOps course covering evaluation benchmarks for LLM applications, task-specific evaluation methodologies, and core tooling. Includes hands-on code demos using the DeepEval open-source framework. The post contextualizes why LLM evaluation differs fundamentally from traditional ML: outputs are probabilistic, models are often external, and there is no single correct answer to compare against. It frames LLMOps as a discipline that extends MLOps to address new realities like per-token cost structures, hallucination monitoring, and prompt brittleness.

A Foundational Guide to Evaluation of LLM Apps (Part B)