The guide explores seven key metrics to measure LLM performance in generative AI systems. These metrics provide standardized ways to assess model capabilities, identify weaknesses, and track improvements over time. Unlike traditional ML models with clear right or wrong answers, LLMs generate diverse outputs that require multidimensional evaluation. The metrics cover operational performance (latency, throughput), generation quality (perplexity, cross-entropy), token usage, resource utilization, and reliability (error rates). Each metric offers a unique perspective on the model's strengths and weaknesses, allowing teams to optimize their LLM systems for specific use cases and applications.