LLM Evaluation, Benchmarking & Red Teaming

Systematically measure accuracy, groundedness, and hallucination rates using LLM-as-a-Judge and golden test sets.

The Modern LLM Evaluation Stack

Unit testing probabilistic language models requires automated evaluation pipelines:

Groundedness: Does the response cite only facts present in the retrieved context?

Answer Relevance: Does the answer directly address the user's prompt without tangential fluff?

LLM-as-a-Judge: Using a larger frontier model (e.g. GPT-4) with strict rubric prompts to grade smaller model outputs.

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In RAG evaluation, what metric measures whether every claim in the response is supported by the retrieved context?