Evals: how to tell if your model is improving

A repeatable eval — collecting example inputs, defining what a good answer looks like, and scoring outputs consistently — lets teams compare two prompts or two models with numbers rather than impressions, according to guidance on building evaluations for AI systems. Scoring typically combines programmatic checks such as valid JSON and required fields, human review, and an LLM acting as judge; because LLM judges tend to favor longer or more confident answers, the guidance recommends checking them against human ratings. Hugging Face's `evaluate` and `lighteval` libraries cover many standard model-level metrics, but a custom dataset built from a team's own use case usually matters more than any public benchmark.