Document and Evaluate LLM Prompting Success

Document and Evaluate LLM Prompting Success is an intermediate course for ML engineers and AI practitioners responsible for the stability and performance of live LLM systems. Moving an LLM from a cool prototype to a reliable production service requires more than just clever prompting—it demands operational discipline. This course provides the framework for that discipline. You will learn to create professional-grade operational documentation, authoring a step-by-step run-book for managing critical system tasks like a vector index update, complete with validation checks and rollback procedures. You will also move from prompt artistry to prompt science, learning to systematically evaluate and A/B test prompt patterns. By analyzing the trade-offs between response quality, consistency, and token cost, you will make data-driven decisions that ensure both performance and efficiency. The course culminates in creating an LLMOps Production-Readiness Toolkit, equipping you to manage and optimize production AI systems effectively

• Create detailed operational run-books for LLM systems
• Evaluate prompt patterns to enhance performance and reduce costs
• Develop validation checks and rollback procedures for critical updates
• Apply A/B testing methods to analyze response quality and consistency
• Make data-driven decisions balancing performance and efficiency

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work