Eval Frameworks

Build an eval framework from scratch for three LLM products (support bot, code assistant, summarizer). Cover architecture, shared infra, per product customization, and six month failure modes.

HELM from Stanford claims to be a 'holistic' evaluation. What makes it different from running MMLU alone?

You hear about 'eval harnesses' like Promptfoo and DeepEval. Explain what an eval harness does that a Jupyter notebook cannot.

Compare Promptfoo, DeepEval, and LangSmith as LLM eval frameworks: when to use each?

Match each LLM eval framework to its primary differentiating capability