May 2026
Intermediate
340 pages
4h 44m
Chinese
Overview
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大型语言模型（LLMs）改变了自然语言处理的方式，但在应用中部署这些模型却带来了众多技术挑战。大型语言模型：困难部分》对开发人员和人工智能工程师在构建基于 LLM 的应用程序时所面临的限制进行了清晰、实用的探讨。本书的重点是实施陷阱（而不仅仅是能力），提供了由可重现的 Python 代码和开源工具支持的可行策略。
读者将学习如何克服应用评估、输入管理、测试和安全方面的关键障碍。本指南专为构建者和技术产品负责人设计，强调针对现实世界问题的实用解决方案，促进对 LLM 限制和权衡的深入理解。
- 为非确定性系统设计测试和评估策略
- 管理上下文、RAG 和长上下文检索
- 解决输出不一致和结构不可靠问题
- 实施安全和内容节制框架
- 探索对齐挑战和缓解技术
- 在本地利用开源模型
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