Document and Evaluate LLM Prompting Success

Overview

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.

Syllabus

Authoring an LLM Operations Run-book

In this foundational module, learners will explore the critical importance of clear and actionable documentation in the management of production AI systems. They will delve into the reasons why robust documentation is essential, transitioning from a conceptual understanding to the practical creation of a professional-grade run-book. Through a blend of instructional videos, targeted readings, and engaging dialogues, learners will identify key components of effective documentation, adhere to best practices in technical writing, and apply these insights to a realistic scenario: managing a vector index update for a large language model (LLM) system. By the end of the module, participants will be equipped to construct a comprehensive run-book that enhances operational clarity and facilitates effective collaboration among both technical and non-technical stakeholders.

Evaluating Prompt Engineering Patterns

This module transitions from system stability to performance optimization by focusing on prompt engineering as a systematic discipline. Learners will discover why ad-hoc prompting fails in production and will learn a structured framework for comparing patterns like Zero-Shot and Few-Shot. They will analyze trade-offs between quality, cost, and consistency, and practice communicating their findings in a format suitable for a team-wide "lunch-and-learn," addressing the second and third learning objectives.