Best Practices and Methods for LLM Evaluation

Evaluating large language models requires a combination of quantitative metrics (like BLEU, ROUGE, perplexity) and qualitative assessments through human judgment. Effective evaluation relies on three components: metrics, high-quality datasets, and structured frameworks. Methods include reference-based metrics that compare outputs to correct answers, reference-free metrics that assess intrinsic text quality, and LLM-as-a-Judge techniques. Key challenges include domain-specific relevance, handling correct but non-standard responses, and evaluating few-shot/zero-shot learning capabilities. Databricks introduced Mosaic AI Agent Framework to address enterprise-scale evaluation needs for complex AI systems like agents and RAG pipelines, providing tools for quality, cost, and latency assessment from development through production.