October 2026
346 pages
5h 1m
Chinese
Overview
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AI 代理需要在恰当的时机获得恰当的上下文信息，才能发挥良好作用。输入过多会增加成本并降低准确性，而输入过少则会导致系统不稳定并产生“幻觉”。《DSPy 下的上下文工程》提出了一种实用且以评估为导向的方法，用于设计在扩展过程中仍能保持可靠、可预测且易于维护的 AI 系统。
AI工程师兼教育家迈克·泰勒（Mike Taylor）以清晰易懂的风格讲解了DSPy，展示了其模块化结构、可移植程序和内置优化器如何帮助团队摆脱凭空猜测的困境。 通过真实案例和分步指导，您将了解 DSPy 的签名、模块、数据集和指标如何协同工作，以解决随着模型变化和工作负载扩展而不断演变的上下文工程问题。
本书为人工智能工程师、数据科学家、机器学习从业者以及软件开发人员提供支持，帮助他们构建能够稳定运行于生产环境的人工智能代理、检索增强生成（RAG）系统以及多步骤推理工作流。
- 理解上下文工程背后的核心理念及其重要性
- 利用 DSPy 可维护、可重用的组件构建 LLM 管道
- 应用 GEPA 和 MIPROv2 等基于评估的优化器，实现可量化的性能提升
- 利用清晰的指标创建可重现的 RAG 和智能体工作流
- 开发能够在不同服务提供商、模型更新及现实世界约束条件下保持稳健的AI系统
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