From 7a459cd702994ddd3455216e335eba89cbdb7e74 Mon Sep 17 00:00:00 2001 From: Phodal Huang Date: Tue, 27 Aug 2024 21:09:10 +0800 Subject: [PATCH] Add Agent debugging chapter and case studies: Introduce a new chapter on Agent debugging and add case studies using PromptLayer for debugging and optimization. --- .shire/research/placeholder.md | 6 +++ src/SUMMARY.md | 1 + src/agent-debug.md | 88 +++++++++++++++++++++++++++++++++ src/images/prompt-ab-test.png | Bin 0 -> 32823 bytes src/images/prompt-router.png | Bin 0 -> 103434 bytes 5 files changed, 95 insertions(+) create mode 100644 .shire/research/placeholder.md create mode 100644 src/agent-debug.md create mode 100644 src/images/prompt-ab-test.png create mode 100644 src/images/prompt-router.png diff --git a/.shire/research/placeholder.md b/.shire/research/placeholder.md new file mode 100644 index 0000000..6c82011 --- /dev/null +++ b/.shire/research/placeholder.md @@ -0,0 +1,6 @@ +# PromptLayer 案例 + +- [How Ellipsis uses PromptLayer to Debug LLM Agents](https://blog.promptlayer.com/how-ellipsis-uses-promptlayer-to-debug-llm-agents/) +- [How Speak Empowers Non-Technical Teams with Prompt Engineering and PromptLayer](https://blog.promptlayer.com/how-speak-empowers-non-technical-teams-with-prompt-engineering-and-promptlayer/) +- [How PromptLayer Enables Non-Technical Prompt Engineering at ParentLab](https://blog.promptlayer.com/how-promptlayer-enables-non-technical-prompt-engineering-at-parentlab/) +- [Gorgias Uses PromptLayer to Automate Customer Support at Scale](https://blog.promptlayer.com/gorgias-uses-promptlayer-to-automate-customer-support-at-scale/) diff --git a/src/SUMMARY.md b/src/SUMMARY.md index a8cb194..0b63cb1 100644 --- a/src/SUMMARY.md +++ b/src/SUMMARY.md @@ -60,6 +60,7 @@ - [代码问答 Agent 示例](./agent-codebase-example.md) - [Copilot Workspace](./agent-copilot-workspace.md) - [RAG 数据与 AI 安全](./agent-data-security.md) + - [Agent 调试](./agent-debug.md) - [第 5 部分:AI 自动化编程](./flow-aise.md) - [理解 AI 自动化编程](./flow-in-aise.md) - [自动化 CRUD](./flow-example-autocrud.md) diff --git a/src/agent-debug.md b/src/agent-debug.md new file mode 100644 index 0000000..8559ddc --- /dev/null +++ b/src/agent-debug.md @@ -0,0 +1,88 @@ +# Agent 调试 + +## Prompt(提示词) 测试 + +### Prompt 单元测试 + +> 验证智能体内部的一个小部分,例如验证智能体在明显的情况下调用了特定的工具。适合测试晦涩的场景,容易验证预期行为。 + +### Prompt 集成测试 + +> 从头到尾的复杂场景。这更有价值,因为它能告诉你“它实际上是否有效”。但这往往难以编写断言,因为结果是模糊的,例如对一个模棱两可的问题的回答。 + +## 示例 + +### Ellipsis + +[Lessons from 15 months of building LLM agents](https://www.ellipsis.dev/blog/lessons-from-15-months-of-building-llm-agents) + +- 评估与测试:作者强调了评估(evals)在开发LLM代理过程中的重要性。评估有两种类型:单元测试和集成测试。单元测试检查代理的某个小部分,而集成测试则验证复杂场景的整体表现。由于提示词(prompts)非常脆弱,甚至一个小的修改都可能导致输出质量下降,因此反复测试和验证是确保代理成功的关键。 +- 快照与缓存:为了应对LLM的非确定性和高成本,作者建议在开发代理时设置缓存。缓存不仅能使测试结果更具确定性,还能显著加快速度并降低成本。 +- 可观测性与日志记录:作者指出,尽管有许多高级的观测工具,但他仍然发现传统的日志记录更为有用。日志记录能够帮助开发者直观地理解代理的行为并诊断问题。 +- 提示词的管理与优化:作者认为提示词必须在版本控制中进行管理,尤其是对于复杂的代理而言。此外,他认为现有的LLM代理构建库(如Langchain)在实际应用中并不实用,代理往往需要大量的定制,因此自己编写代理逻辑可能更加高效。 + +### PromptLayer + +https://promptlayer.com/ + + +PromptLayer不仅仅是一个调试工具,它还是一个强大的提示工程和管理平台,提供了多种关键功能,使得AI应用的开发和运营变得更加高效和智能化。 + +1. **提示工程与管理**:PromptLayer提供了强大的提示版本控制、批量处理和调试工具,使得团队能够更快地迭代和优化提示,确保AI模型能够提供更准确和上下文相关的响应。 +2. **可观察性与分析**:通过详细的日志和元数据分析,PromptLayer帮助团队更好地理解AI的行为模式,发现潜在的问题并及时进行优化,减少了AI系统中的“黑箱”操作,增强了透明度。 +3. **可视化编辑与部署**:PromptLayer的可视化编辑和自动部署功能,使得即使是非技术团队也能够高效地进行提示工程。这减少了对工程资源的依赖,提升了团队的整体生产力和灵活性。 + +#### PromptLayer 案例 + +案例: + +| **公司名称** | **挑战** | **PromptLayer解决方案** | **结果** | +|---------------|--------------------------------|----------------------------------------------|----------------------------------| +| **Ellipsis** | 快速扩展带来的高请求量导致调试效率低下 | 使用PromptLayer的可观察性平台和元数据搜索功能,快速定位问题并通过UI进行调试 | 调试时间减少90%,调试步骤减少90%,避免了大量工程资源消耗 | +| **ParentLab** | 内容和工程团队需要快速迭代和部署AI提示 | 通过PromptLayer的CMS和可视化编辑功能,简化提示迭代和部署流程 | 提示部署速度提升10倍,节省400多个工程小时,提示质量显著提高 | +| **Speak.com** | 非技术团队需独立迭代AI功能,但缺乏工程支持 | 使用PromptLayer,内容和业务团队可以自主进行提示工程,减少开发时间 | 产品开发和国际扩张中的大量时间和成本节省,关键功能迭代更快速灵活 | +| **ParentLab** | 需要分析用户行为和优化AI响应,但Notion数据库效率低下 | PromptLayer提供日志和分析功能,帮助理解用户行为并持续改进AI响应 | 提示迭代次数增加10倍,提示质量显著提高,节省数百个工程小时 | + +#### Prompt Router + +- [Prompt Routers and Modular Prompt Architecture](https://blog.promptlayer.com/prompt-routers-and-modular-prompt-architecture-8691d7a57aee/) + +![](images/prompt-router.png) + +有诸多方法可以编程决定将用户的消息路由至哪个提示。 + +1. 通用大型语言模型(LLM):最简单且最常见的方法是使用如GPT-4这样的大型语言模型。以下我们会采用这种方法。简而言之,创建一个提示模板,该模板接收用户消息,并返回用于路由的提示模板。 +2. 微调模型:通常,如果您能够可靠地获得GPT-4提示作为路由器,您可以微调一个较小的模型以节省成本和延迟。尝试对您的GPT-4输出进行GPT-3.5-Turbo的微调。 +3. 向量距离:生成嵌入向量非常快速且成本低廉(与常规完成相比)。您可以通过比较用户消息与可能分类的向量距离,轻松构建一个提示路由器。 +4. 确定性方法:有时您可以通过确定性解决方案来决定如何路由。也许您正在字符串中搜索一个关键词…… +5. 传统机器学习:最后但同样重要的是,分类是传统机器学习非常常见的一个应用场景。像决策树这样的工具可以帮助您构建比通用大型语言模型更便宜、更快速的提示路由器。 + +为了简化,我们将使用第一种选项,并为GPT-4编写一个作为路由器的提示。我们的提示模板将接收一个用户问题,并返回一个单词,指示适当的分类:“自我”、“新闻”或“编程”。在您的应用程序代码中,您可以使用这个输出来将问题映射到相应的提示模板。 + +#### Prompt 管理 + +- [Scalable Prompt Management and Collaboration](https://blog.promptlayer.com/scalable-prompt-management-and-collaboration/) + +这篇文章强调了在使用大型语言模型(LLM)开发AI产品时,提示管理的重要性。文章指出,尽管提示是使基于LLM的产品有效运行的关键,但往往被忽视, +导致产品演进过程中出现效率低下和瓶颈问题。 + +1. **将提示与代码分离**:将提示与主代码库分离存储,可以加快迭代速度,并使非技术人员更容易参与协作。使用提示管理系统(如PromptLayer)有助于简化这一过程。 +2. **模块化设计**:文章借鉴了软件开发中的原则,建议将提示模块化为可重用的组件,如系统消息前言、少样本示例和指令。这种模块化方法简化了提示管理,使非技术人员更易参与。 +3. **工作区组织**:保持提示在文件夹中的有序组织和控制访问,对于维护整洁高效的工作流程至关重要。良好的组织减少了风险,增强了协作效率。 +4. **协作策略**:有效的提示管理需要建立编辑权限控制机制和版本冻结,以确保工程师与非技术人员之间的协作既稳定又安全。 + +#### A/B Testing Prompts + +Prompt AB Test + +![](images/prompt-ab-test.png) + +那么,究竟如何进行提示语的A/B测试呢? + +- 首先,从小规模开始:先将新版本的提示语推向一小部分用户,例如5-10%的用户,或者仅仅是免费层级的客户。这样做可以限制风险,同时还能为您提供有价值的数据。 +- 逐步增加:在慢慢提高推广百分比的同时,密切关注用户指标。目标可以是10%、20%、40%,最终达到100%。注意任何红旗警告或负面影响。 +- 细分用户:并非所有用户都相同。考虑根据用户类型(免费用户与付费用户)、用户ID或公司等因素进行用户细分。这可以让您在全面推广之前,针对特定群体测试新版本。 +- 多种方法结合:A/B测试很有力,但不要仅限于此。将其与合成在线评估和严格的线下测试结合,以全面了解提示语的表现。 +- 迭代和优化:A/B测试是一个持续的过程。分析您的结果,对提示语进行调整,然后再次运行测试。持续的迭代是🗝️打造顶级提示语的关键。 + 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