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