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LLM Evaluation: Building Reliable AI Systems at Scale

How Formula-Based Metrics Fit into LLM Evaluation

Discover how formula-based metrics such as BLEU, ROUGE, and perplexity serve as useful tools for comparing models and benchmarking in research. Learn why these metrics fall short for real-world AI systems that require evaluation of retrieval, reasoning, and user goal success. Understand how to balance metric use with trace-driven failure analysis for reliable production LLM evaluation.

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Many practitioners new to LLM evaluation expect formula-based metrics, such as BLEU, ROUGE, METEOR, BERTScore, and perplexity, as well as other research benchmarks used to compare models. Because these metrics dominate blogs, tooling guides, and academic papers, they are often assumed to play a central role in evaluating real products. A search for “LLM evaluation” quickly surfaces summaries of these approaches, raising questions about how the resulting scores fit into a practical evaluation workflow.

This lesson clarifies how these metrics fit into a practical evaluation workflow. Formula-based metrics have a clear purpose. They are useful for comparing models, measuring progress on fixed datasets, and standardizing evaluation in research settings. However, they rely on assumptions such as single-reference answers, fixed targets, and output-only scoring, which rarely hold in production systems that involve retrieval, tools, and multi-step reasoning. Rather than ignoring these metrics, the lesson explains where they are applicable, where they fall short, and how they complement the trace-first, failure-driven approach emphasized throughout the course.

Where formula-based metrics fit (and where they don’t)

Most people searching for “LLM evaluation” encounter material about model-level metrics such as BLEU, ROUGE, METEOR, BERTScore, perplexity, MMLU, MT-Bench, and other statistics used to benchmark foundation models. These metrics originate from the NLP tradition of comparing model output to a fixed reference answer, typically in translation, summarization, or question-answering benchmarks. They work best ...