The text discusses the critical distinctions between various AI technologies, including artificial intelligence (AI), machine learning (ML), large language models (LLMs), and generative AI, highlighting their unique capabilities, resource requirements, and suitable applications within enterprises. It emphasizes the importance of understanding these differences to avoid costly errors in implementation, such as budget overruns and mismatched skills, and to ensure successful deployment. AI serves as the overarching category, with ML focusing on data-driven predictions, LLMs specializing in natural language processing, and generative AI handling creative tasks across multiple modalities. The text outlines the distinct operational, infrastructure, and data requirements for each technology, stressing the need for specialized hardware and comprehensive infrastructure to support advanced AI workloads. It also underscores the importance of matching the right technology to specific business problems and data availability to maximize impact while maintaining cost-effectiveness. Additionally, it introduces Galileo's evaluation platform as a tool for monitoring AI deployments, ensuring compliance, and mitigating risks through real-time assessments and proactive guardrails.