Integrating generative AI into business processes poses challenges due to the probabilistic nature of AI outputs, which often leads to hallucinations and requires extensive manual review. To address this, a systematic approach involving measurement infrastructure is advocated, starting with tracking hallucination rates and completeness. The CLEAR framework, which emphasizes prompts that are Concise, Logical, Explicit, Adaptive, and Reflective, forms the foundation for effective optimization. Techniques such as Chain-of-Thought prompting, few-shot examples, rule-based self-correction, multi-step workflows, dynamic context optimization, and adversarial testing are recommended to improve AI accuracy and efficiency. These methods aim to reduce hallucinations, enhance reasoning, and ensure up-to-date responses, ultimately achieving a reliable, measurable process. Platforms like Galileo provide the technical infrastructure necessary for comprehensive evaluation and optimization, facilitating the deployment of AI systems with confidence in production environments.