AI/LLM App Evaluation Cheat Sheet

AI and LLM application evaluation is the practice of systematically assessing the quality, safety, and performance of large language model applications across development and production environments. Unlike traditional software testing, LLM evaluation requires measuring subjective qualities like relevance, coherence, and factual accuracy alongside objective metrics like latency and cost—making it both an engineering and human-centered discipline. In 2026, evaluation has expanded beyond single-turn outputs to encompass multi-turn conversations, agentic workflows, and multi-modal systems, with dedicated metrics, benchmarks, and platforms for each layer. The key insight: what you don't measure, you can't improve—systematic evaluation transforms LLM applications from unpredictable experiments into reliable production systems.