the complete guide for LLM evaluations in 2026 | Galtea Blog

The text discusses the evaluation of language model (LLM) applications, focusing on assessing whether a model meets the specific needs of an application rather than general benchmarks like MMLU or HellaSwag. It emphasizes evaluating functional quality, safety, and production stability across distinct layers and stages, using methods like reference-based metrics, LLM-as-a-judge, and human evaluation. The importance of structured traces, golden datasets, and continuous monitoring is highlighted to identify and address specific failure modes. It also warns against common pitfalls such as optimizing metrics over tasks, relying solely on post-event evaluations, and conflating model quality with application performance. The text underscores that evaluation is a continuous, nuanced process requiring tailored criteria and methodologies to ensure LLM applications perform reliably in real-world contexts.

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