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Document and Evaluate LLM Prompting Success

本课程是 LLM Optimization & Evaluation 专项课程的一部分

位教师：LearningMate

包含在中

了解更多

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

2 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Create operational run-books for LLM systems and evaluate prompt patterns to improve performance and reduce operational costs.

您将获得的技能

您将学习的工具

Prompt Engineering

要了解的详细信息

可分享的证书

添加到您的领英档案

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 LLM Optimization & Evaluation 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有2个模块

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.

单元详情

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.

涵盖的内容

1个视频1篇阅读材料2个作业

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.