Monitoring Generative AI: Control Costs and Ensure Quality

As generative AI applications become central to many operations, understanding how to monitor their performance, cost, and output quality is crucial. This course will guide you through establishing comprehensive monitoring strategies for your GenAI applications, enabling you to proactively manage expenses, maintain high output quality, and build trust in your AI systems. What you'll learn: Understand the core concepts of generative AI application monitoring, including key metrics and challenges. Implement strategies for tracking token usage and managing the operational costs of large language models (LLMs). Apply methods for evaluating and monitoring the quality of GenAI outputs, such as relevance, coherence, and safety. Configure basic observability patterns for GenAI, including logging and tracing LLM interactions for deeper insights. Practice techniques for prompt versioning and management to ensure consistent model behavior and performance over time. Develop a framework to ensure your GenAI applications are both cost-effective and deliver reliable, high-quality results. The course begins with foundational concepts, then progresses through practical techniques for cost tracking, quality assessment, and implementing observability, concluding with strategies for continuous improvement. This course is designed for beginners interested in understanding and implementing monitoring for generative AI applications. No prior experience with AI monitoring is required. Start building robust and efficient GenAI applications today by mastering effective monitoring.