1. What’s the difference between a RAG chatbot and a traditional chatbot?

The difference between a RAG chatbot and a traditional chatbot is that a traditional chatbot relies on static responses or pre-trained models, while a RAG (retrieval-augmented generation) chatbot pulls relevant data from an external knowledge base and uses it to generate more accurate responses in real time.

2. How does a RAG system decide what knowledge to retrieve vs. what to generate?

A RAG system uses a retriever to identify the most relevant documents or data chunks based on the user’s question, then passes that information to a language model which generates a response grounded in the retrieved content, rather than relying purely on prior training.

3. Can I use RAG without uploading external knowledge sources?

You can technically use RAG without uploading external knowledge sources, but doing so nullifies its core advantage. RAG is designed to supplement the language model with domain-specific or real-time data, so omitting external knowledge defeats its purpose.

4. Is RAG suitable for multilingual applications?

Yes, RAG is suitable for multilingual applications as long as both the language model and the retrieved documents support the target languages; many modern LLMs and vector databases are capable of handling multilingual queries and content.

5. How can I measure the accuracy and relevance of retrieved knowledge?

You can measure the accuracy and relevance of retrieved knowledge by logging which documents were retrieved, comparing the generated answer to ground truth or expert feedback, and tracking metrics like response correctness, user satisfaction, and click-through or follow-up rates.

如何构建RAG聊天机器人（2026）

作者

Aryan Kargwal

AI开发者、博士候选人、内容创作者（edtr通讯 & Botpress）

摘要

RAG聊天机器人结合了检索与语言生成技术，比传统机器人能提供更准确、更具上下文的回答。
RAG通过访问外部数据来减少错误并提升可靠性——而不是仅依赖语言模型自身的知识。
创建RAG机器人需要搭建项目、编写清晰的行为指令，并连接知识源。

聊天机器人正在重塑企业与用户的互动方式，但许多机器人在理解上下文和准确性方面仍有不足。这正是检索增强生成（RAG）发挥作用的地方。

RAG结合了知识检索与语言生成的优势，使聊天机器人能够访问外部数据，提供精准且具备上下文的答案。结果如何？更少的错误、更高的效率、更可靠的回复。

过去几年，我们已经部署了超过75万个聊天机器人——而且我们还为它们开发了专属的先进RAG流程。

因此，在本指南中，我将带你一步步构建自己的RAG驱动聊天机器人——从定义行为到无缝部署。‍

构建AI聊天机器人

打造定制化智能代理聊天机器人

立即开始

RAG聊天机器人的应用场景

你是否正准备将RAG聊天机器人集成到你的产品、项目或兴趣中？让我们来看看可以通过定制智能机器人提升的广泛应用场景。

类别	简介	示例用途
客户支持	从政策、常见问题和指南中获取精准答案。	“什么是漫游费？”或“帮我修复路由器。”
教育工具	即时访问教材、研究资料和课程内容。	“解释一下相对论”或“工业革命？”
法律辅助	基于案例法、法规和文件提供建议。	“违约的处罚？”或“如何注册商标？”
人力资源支持	人力资源信息：政策、福利、培训材料。	“年假天数？”或“如何加入健康保险计划。”

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阅读 RAG 完整指南

想让RAG为您发挥作用？我们可以帮您。

立即阅读

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使用Botpress创建RAG聊天机器人非常简单。以下是详细的分步指南，助你快速上手。

本教程将以创建一本图书问答机器人为例。完成后，你可以通过相关集成将该机器人部署到多个渠道。

如何用RAG构建AI智能体

第1步：搭建你的项目

首先，在Botpress中新建一个项目。登录后，选择“从零开始”模板，以获得完全的自定义和灵活性。

第2步：编写清晰的指令集

指令对于RAG聊天机器人的行为至关重要，可在Studio的“指令”部分进行编辑。明确机器人应如何检索和呈现信息。

编写指令集时，请涵盖以下要点：

明确机器人应优先使用外部知识源，而非模型内部数据。
说明回答的语气和风格，最好通过示例对话和回复来体现。

以下是“服装网站FAQ”聊天机器人行为的指令集示例：

Mission:
Assist users in exploring sustainable fashion, understanding eco-friendly materials, and making informed choices about ethically sourced clothing.

Personality Traits:
- Knowledgeable: Provides accurate answers from the knowledge base.
- Friendly: Warm and approachable.
- Inspiring: Encourages sustainable choices.
- Transparent: Shares only validated information.

Capabilities:
- Educate: Explain sustainable fashion principles, eco-friendly materials, and certifications using the knowledge base.
- Assist: Recommend products and provide information strictly from the approved data.
- Inspire: Highlight the benefits of ethical fashion without personal opinions.
- Engage: Respond to queries clearly and politely, redirecting users to support if answers are unavailable.

Tone:
- Positive, professional, and jargon-free.
- Respectful and empathetic to ensure a supportive experience.

Behavioral Rules:
- Use only the provided knowledge base (e.g., Wikipedia, Green Threads documents).
- If an answer isn’t available, inform the user and suggest consulting Green Threads’ customer support.

Example Response Policy:
Query: "What is organic cotton?"
Answer: "Organic cotton is grown without harmful pesticides or synthetic fertilizers, reducing environmental impact."

Query: "Can you tell me your refund policy?"
Answer: "I don’t have that information. Please visit our website or contact support for assistance."