When building AI systems, especially those that interact with users or make decisions, it's crucial to measure performance in ways that align with your goals. Galileo provides a comprehensive suite of evaluation metrics out of the box, as well as the ability to create custom metrics via LLM-as-a-Judge or code-based scoring. These metrics are designed to answer specific questions about your AI's behavior. To choose the right metrics, start by identifying your goals and considering what matters most for your use case. Mix and match different categories of metrics, establishing baselines, tracking trends, setting thresholds, and monitoring changes as you iterate. Response quality metrics evaluate how well the model understands and responds to prompts, particularly in terms of factual accuracy, completeness, and adherence to instructions. Safety and compliance metrics watch for danger zones like leaked sensitive information, biased or toxic language, and attempts to manipulate your model via prompt injections. Model confidence metrics quantify uncertainty in responses and assess prompt complexity, while agentic metrics track how well your AI agent navigates multi-step tasks, makes decisions, and uses tools. Expression and readability metrics measure the ✨vibes✨—aka your AI-generated content's tone, fluency, clarity, and human-likeness. Custom metrics allow you to define and register your own evaluation criteria, tailored to your specific needs. By understanding what each metric category measures and when to use it, you can tailor your evaluation strategy to your specific goals and deliver more effective AI experiences.