内容来源：https://blog.n8n.io/ai-workflow-builder-best-practices/

内容总结：

近日，n8n面向Starter、Pro及Enterprise Cloud版本用户推出了AI工作流构建器，该功能可将自然语言指令转化为可运行的自动化流程。

用户无需从零开始搭建工作流，仅需通过简单描述即可在数分钟内将构想转化为实际可用的自动化流程。该工具同时支持对现有工作流进行调试与优化，并在每个步骤提供实时反馈，如同一位随时可协作的“思考伙伴”，既能帮助验证想法、探索潜在节点方案，也能像专家一样协助修复或改进工作流。

为帮助用户更高效地使用该功能，n8n结合社区反馈与AI工程团队经验，总结出以下实践建议：

1. 采用迭代构建思维
   对于复杂自动化流程，建议先通过指令生成初步流程框架，再通过“运行-调整-优化”的循环逐步完善，避免一次性输入过长指令。

2. 提前准备参数与凭证
   多数工作流需手动补充API密钥等认证信息。建议提前规划所需连接的工具与服务，预先准备登录信息以减少构建中断。

3. 谨慎使用外部AI生成指令
   依赖其他AI工具生成的冗长指令可能无法精准体现触发条件、数据流等关键设计。建议以清晰的目标为导向，通过多次测试迭代优化指令。

4. 明确集成节点与数据流向
   指令需具体明确，例如避免使用“获取邮件”等模糊表述，而应说明“从Gmail账户获取最近10封邮件”。同时需清晰描述数据来源、处理逻辑及输出目标，例如“从Gong获取最近100条销售通话摘要，经OpenAI提炼为2-3个要点后，存储至n8n数据表并标注日期、公司名称”。

5. 无需角色设定说明
   该工具已具备专业工作流构建能力，无需额外添加“你是一名专家”等场景化描述指令。

成功指令示例分析
一条优质指令通常具备以下特征：明确指定集成工具（如OpenWeather、Gmail）；清晰描述从触发到结束的完整流程；指令简洁（如低于400字符），便于快速验证与调整。

核心提示
高效使用AI工作流构建器的关键在于：以具体、清晰的指令驱动构建，提前规划节点与集成需求，并通过迭代式开发逐步完善。避免追求“一次性完成”，灵活调整方向更能提升构建效率。

欢迎社区用户分享更多使用技巧，现在就查阅文档开始构建您的自动化工作流吧！

中文翻译：

我们近期为Starter、Pro及Enterprise Cloud用户推出了AI工作流构建器，可将您的自然语言指令转化为可运行的自动化流程。

无需再从空白画布开始从头构建新工作流，您能在数分钟内将脑海中的自动化构想转化为实际可用的工作流。您甚至可以用它来调试和完善现有工作流，AI工作流构建器会在每个阶段提供实时反馈。

不妨将AI工作流构建器视为您的思维伙伴：它既是验证想法的回音板，也是探索未曾考虑节点的导航仪，更是一位随时待命的n8n专家，助您修复或优化工作流。

通过指令构建工作流是自然直观的过程，但要充分发挥AI工作流构建器的潜力，往往需要多次迭代才能找到最适合您的方式。

如何高效使用AI工作流构建器因人而异。基于n8n社区及AI工程团队的反馈，我们总结出以下经过验证的提示词优化方法：

最佳实践与范例

迭代式构建思维
虽然完整规划工作流的触发条件、数据处理逻辑及最终呈现形式非常理想，但将所有要求塞进一条冗长指令可能适得其反。

对于大型复杂自动化项目，请采用迭代思维。先用AI工作流构建器创建初步流程框架，随后通过运行、调整指令、优化细节的循环逐步完善。

预先准备必要参数与凭证
首次发送指令启动工作流构建时，绝大多数工作流都需要您手动补充参数和凭证——特别是当涉及需要通过特定账户访问数据的API和工具时。

AI工作流构建器完成每轮构建后，会详细列出需要手动配置的步骤以确保当前版本正常运行。

在规划整体工作流时，请提前梳理计划连接的工具服务，备妥登录信息与访问权限，从而减少构建过程中的中断。

慎用外部AI生成的提示词
虽然借助其他AI工具打磨提示词看似便捷，但这往往会导致指令冗长，却仍可能面临与简短指令相同的问题——对触发机制、输出形式及数据流缺乏周密规划。

使用其他聊天工具辅助构思并无不妥，但切勿依赖其一次性生成完美工作流的全能指令。请保持迭代思维：明确工作流目标，通过指令、测试、优化的循环逐步推进。

明确集成方式与节点类型
避免使用“获取我的邮件”或“发送结果到表格”这类模糊表述，而应具体说明需求：“从我的Gmail账户获取最近10封邮件”“发送至名为XXX的Google Sheets表格”。精确的指令能帮助AI工作流构建器准确理解意图，减少迭代次数，并提供更具操作性的后续步骤建议。

清晰描述数据流转路径
如同明确集成方式，请尽可能详细说明数据在工作流中的预期流转过程。

在指令中明确告知AI工作流构建器：数据来源、处理方式及输出目标。

例如，将“获取最新销售通话记录并总结后保存”优化为：“从Gong获取最近100条销售通话文本摘要，发送至OpenAI生成每条2-3个要点的总结，将包含通话日期、公司名称和AI总结的结果保存至n8n数据表”。

同时需明确数据格式要求及节点间传递的字段信息。

无需角色扮演说明
与某些需要向AI解释操作方式才能获得最佳效果的通用聊天工具不同，AI工作流构建器无需此类铺垫。

不必强调“你是一位专业的n8n工作流构建专家”——它本就具备此能力，会根据您的指令创建最优工作流。

优质提示词深度解析
范例指令：“创建一个自动化流程，每天清晨5点通过OpenWeather检查当地天气，使用Gmail发送简短天气邮件，并调用OpenAI撰写生动有趣的邮件正文，在描述天气和当日体感时注入个性化表达，包含所有影响日程安排与着装决定的详细信息。”

成功要素分析：

1. 明确指定集成工具
   指令中直接提及Gmail、OpenWeather等具体工具而非泛称“邮件服务”“AI模型”，确保生成对应功能节点。

2. 完整描述端到端流程
   自动化流程的每个阶段都有清晰定义，无需AI工作流构建器猜测补充。

3. 简洁直白的任务指令
   无需附加AI语境说明或场景设定，指令本身已包含全面操作指引。

4. 便于迭代的轻量设计
   指令长度控制在400字符内（而非千字长文），便于快速验证构想并随需求变化灵活调整。

核心要点总结
AI工作流构建器遵循“输入决定输出”原则：清晰、明确、具体的提示词将生成结构严谨、从触发到输出脉络分明的工作流。依赖外部工具加工提示词可能反而增加理解成本与构建时间。

建议先明确工作流目标及可能需要的节点与集成方式，这有助于规划参数、凭证、API接口等其他需求。

最后请勇于迭代构建：通过简短明确的指令分步创建工作流，而非追求一步到位。这样既能快速验证想法，也能及时调整方向。对节点、集成方式和数据流的精确描述，将有效减少达成理想工作流所需的迭代次数。

您在使用AI工作流构建器时有何心得？欢迎与社区成员分享您的技巧与优质提示词范例。

现在，查阅文档开始使用AI工作流构建器吧！

英文来源：

We recently released AI Workflow Builder for Starter, Pro and Enterprise Cloud customers, turning your natural language prompts into working automations.
Instead of launching into building a new workflow from scratch with a blank canvas, you can easily get your automation ideas out of your head and into a functioning workflow in minutes. You can even use it to debug and refine your existing workflows, all with real-time feedback of what AI Workflow Builder is doing at every stage.
Think of AI Workflow Builder as a thought partner, there to be used as a sounding board for validating ideas, a way of exploring nodes that you might not have considered, and an n8n expert ready to help fix or improve your workflows.
Building workflows with prompts is a natural and intuitive process, but getting the most of AI Workflow Builder can take time and a few iterations to find what works best for you.
Getting the most from AI Workflow Builder is a personal experience. Here are proven ways to improve your AI Workflow Builder prompts, based on feedback from the n8n community and our AI engineering team.
Best practices & examples
Think in iterations
Having a full plan of how you want the workflow to be triggered, what data and logic needs to be processed, and how it end is fantastic. However, giving AI Workflow Builder all of this in a single huge prompt might not get the best outcomes.
Think in iterations, especially for large, complex automations. Use AI Workflow builder to create a rough “map” of the finished workflow, then run, prompt, refine, and repeat.
Prepare for required parameters and credentials
When sending the first prompt to kick-start the workflow building process, almost all workflows will need you to manually enter additional parameters and credentials, especially if using APIs and tools that need access to data via a specific account.
When AI Workflow Builder finishes after your prompt, it will detail all manual steps you need to carry out to make sure this iteration works correctly.
As you’re planning your overall workflow, think about the tools and services you plan on connecting to and plan for any login information or required access in advance to cut down on delays in your workflow building.
Don’t rely on prompts created with other AI chat
It might be tempting to use another AI tool for helping build the perfect prompt, but in reality this can lead to overly-long prompts that might still face the same challenges as a much shorter prompt that lacks good planning for triggers, outputs and data flow.
Creating a prompt using another chat tool isn’t a bad thing, but don’t rely on it to create a single, comprehensive prompt that creates the perfect workflow in one go. Treat it in the same iterative way of prompting, testing, and refining, with a clear vision of what you are wanting the workflow to achieve.
Be specific about integrations and nodes
Don’t just say “get my emails” or “send results to a table”, get ready to be specific with exactly what you want AI Workflow Builder to make for you: vague prompts = vague workflows that will need more iterations and potential manual work to get them working.
Instead, use prompt language like “get the latest 10 emails from my Gmail account” and “send to a Google Sheet called XXX” which will help AI Workflow Builder create exactly what you were meaning, reduce build iterations, and return back more helpful responses for required next steps.
Clearly describe the flow of data
Just like being specific about integrations, try to be as specific as possible about the way you expect data to flow in your workflow.
In your prompts, tell AI Workflow Builder where it should find data, what it should do with it and where it should send it.
Rather than “Get the latest sales calls, summarize them, then save the summary”, use more specific prompting like “Get the last 100 sales call text summaries from Gong, then send them to OpenAI to summarize each one in 2-3 bullet points. Save the summaries in an n8n Data Table with the date of the call, company name and AI summary”.
Be clear about how the data should be formatted, and which fields should be sent from one node to another.
No need to role play
Unlike some general purpose AI chat tools that require you to explain to the agent how it should operate to get the best results, there’s no need to do that with AI Workflow Builder.
Don’t worry about telling it “You are an expert n8n workflow builder” - it already is, and will create the best possible workflows based on your prompts.
A deeper look into why a good prompt works well
Create an automation that checks the weather for my location every morning at 5 a.m using OpenWeather. Send me a short weather report by email using Gmail. Use OpenAI to write a short, fun formatted email body by adding personality when describing the weather and how the day might feel. Include all details relevant to decide on my plans and clothes for the day.
It calls out specific integrations
Here, the prompt is calling out specific tools like Gmail and OpenAI rather then “email” and “AI model”, helping it to create the right nodes for the task.
It clearly describes the flow from start to finish
Nothing is left for AI Workflow Builder to work out, the prompt clearly states what should happen at each stage of the automation.
No-fuss, straight to the point instructions
We don’t need to include additional AI-related instructions or “scene setting” to help with context, here the prompt is instructing AI Workflow Builder what to do with comprehensive instructions.
A small iteration ready for the next step
The prompt is <400 characters rather than a 1,000+ character input, giving you the ability to quickly validate ideas and make small changes as your workflow idea evolves.
Key takeaways
With AI Workflow Builder you really do get out what you put in: clear, well-defined, and specific prompts will create well-built workflows with clear flows from trigger to output. External tools to help you write your prompts might just add confusion and extra build time.
Have a idea in mind of what you want the workflow to achieve and which nodes and integrations you might want, helping you plan for other requirements like parameters, credentials and API endpoints.
Lastly, don’t be afraid to build in iterations: use AI Workflow Builder to create workflows in clear, short prompts rather than trying to achieve everything in one go. This allows you to quickly validate ideas and quickly change direction if needed. Being specific about your nodes, integrations and flow of data will help reduce the number of iterations needed to get to your perfect workflow.
Got any tips on how you're building with AI Workflow Builder? Let other community members know your best tips, tricks and prompts.
Now, check out the docs and get building with AI Workflow Builder!