What is Dify? Complete AI Bot Building Tutorial

Dify is an open-source platform that empowers users to build AI-native applications, such as chatbots, agents, and Q&A systems, using a visual, no-code or low-code interface. It integrates seamlessly with LLMs like OpenAI, Anthropic, Azure OpenAI, and others.

In this guide, we’ll learn how to build our own AI-powered Q&A bot using Dify.ai. The steps include:

Step 1: Create a knowledge base

Step 2: Create a chatflow

Step 3: Configure a knowledge retrieval node

Step 4: Configure the LLM node

Step 5: Chat with the Q&A bot

Let’s explore each of these steps in detail.

Build an AI-powered Q&A bot with Dify

Creating a Q&A bot in Dify is a straightforward process. Before diving into it, let’s have a look at the prerequisites:

1. Dify.ai account (Create one in the official website)
2. OpenAI API key (Create one in the official website)

Once these prerequisites are satisfied, you can get started with the process.

Step 1: Create a Dify knowledge base

The primary step is to create a knowledge base that our bot will reference while answering our queries. To do so, follow these steps:

Step 1.1: In the Dify dashboard, click on Knowledge to navigate to the Knowledge tab.

Step 1.2: Click on the plus (+) icon beside Create Knowledge.

Step 1.3: Click on Import from file.

Step 1.4: Navigate to this link and download the oauth2-framework.md file.

Step 1.5: After the download is complete, drag and drop the file in the shown location.

Step 1.6: Click on Next.

Step 1.7: This page will appear. Here, we can modify the settings for the knowledge base. In this case, we’ll go with the default settings and click on Save & Process.

Step 1.8: We’ll be redirected to another page that confirms the successful creation of the knowledge base. Now, click on Studio and proceed to the next section.

Step 2: Create a Dify chatflow

The next step is to create a chatflow that will define how our queries are processed. To do so, follow these steps:

Step 2.1: Click on Create from Blank located at the top-left corner of the Studio tab.

Step 2.2: Click on Chatflow

Step 2.3: Provide a name for the chatflow (Q&A Bot in this case)

Step 2.4: Click on Create

Here is the initial chatflow. This chatflow consists of three nodes:

• START: Initiates the flow. In a chatflow, this begins when the user sends a message.
• LLM: Receives the user’s message and passes it to the specified language model.
• ANSWER: Displays a message in the chat. It is connected to the LLM node, so the LLM’s response is shown directly to the user.

Step 3: Configure a knowledge retrieval node

Now, we’ll configure a knowledge retrieval node, which will fetch relevant information from the connected knowledge base. To do so, follow these steps:

Step 3.1: Click on the plus (+) icon on the left

Step 3.2: Drag the Knowledge Retrieval node onto the chatflow

Step 3.3: Connect the KNOWLEDGE RETRIEVAL node to the chatflow:

The logic here is simple. This chatflow begins when a user sends a message, triggering the START node. The message is then enriched with relevant context from the connected knowledge base through the KNOWLEDGE RETRIEVAL node. This combined input is processed by the LLM node, and the final response is displayed to the user via the ANSWER node.

Step 3.4: Click on the KNOWLEDGE RETRIEVAL node

Step 3.5: A screen will appear, showing us the settings for the node. Here, click on the plus (+) icon.

Step 3.6: Choose the uploaded file as the knowledge base and click on Add.

Step 4: Configure the LLM node

The last step is to configure the LLM node. To do so, follow these steps:

Step 4.1: Click on Plugins at the top-right corner of the screen

Step 4.2: Click on Marketplace

Step 4.3: Type openai in the search box and locate the OpenAI plugin

Step 4.4: Click on Install to install the plugin

Step 4.5: In the chatflow, click on the LLM node to bring up its configurations. Here, we’ll focus on these settings:

1. MODEL: Choose gpt-4 as the model
2. CONTEXT: Click on the box underneath and choose the result variable from the dropdown that appears.
3. SYSTEM: Insert this prompt in the box underneath

You’re a helpful AI assistant that answers questions based on the context provided. 

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Along with this prompt, type ‘Context:’ and set its value to the Context variable. To find the variable, type ‘/’ in the box and choose the variable from the dropdown that appears.

Step 4.6: Click on this icon to bring up the settings for the model

Step 4.7: In the model settings, click on the box under MODEL and then click on Model Provider Settings in the dropdown that appears.

Step 4.8: This page will appear. Here, click on Setup.

Step 4.9: Enter your OpenAI API key in the box under API Key and then click on Save to save the API key. You can generate an OpenAI API key on the official website.

Here is the final chatflow:

Step 5: Chat with the Q&A bot

Now, it’s time to interact with the bot. To do so, click on the Preview button to bring up the chat box:

Here, we can provide our queries and generate responses for them from our Q&A bot:

Here is a sample interaction:

User: What is OAuth 2.0? 

Bot: OAuth 2.0 is an authorization framework that enables third-party applications to get limited access to an HTTP service—either on behalf of a user (resource owner) or on behalf of itself. It enables secure delegated access without exposing the user's credentials. Key components include the Resource Owner, Client, Resource Server, Authorization Server, and Access Token. 

OAuth 2.0 supports multiple grant types such as Authorization Code, Implicit, Resource Owner Password Credentials, and Client Credentials, each designed for different use cases like web apps, mobile apps, and machine-to-machine communication. 

For enhanced security, it's essential to use HTTPS, validate tokens, avoid storing access tokens in insecure locations, and implement proper expiration and refresh mechanisms. 

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Now that we’re done building our Q&A bot using Dify, let’s discuss why we should choose Dify.ai over other similar platforms.

Why use Dify?

There are several reasons for choosing Dify:

• No-code app creation: Dify allows us to build AI apps with a drag-and-drop interface, meaning we don’t need advanced coding skills to get started.
• Agent workflows: Dify supports autonomous agents that can retrieve data, reason, and complete tasks — helpful for use cases like customer support bots, content generation tools, and internal assistants.
• Built-in knowledge base: Dify supports a customizable knowledge base, allowing our AI app to answer questions based on our documents, URLs, or internal data.
• Easy LLM integration: Dify works with major LLM providers like OpenAI, Azure, Anthropic, Google PaLM, Mistral, and others.
• Memory & context handling: Dify has built-in long-term memory and context management, so our apps can remember previous interactions and perform more complex, multi-turn conversations.

Dify.ai reduces the need for external tools by consolidating everything into one streamlined platform.

Next, let’s see how Dify compares with one of its popular alternatives — Flowise.

Dify vs. Flowise

Both Dify and Flowise are designed to help us build AI apps, but they have different focuses:

Dify.ai is more user-friendly and accessible to non-programmers, while Flowise offers more developer-oriented control.

Dify vs. other popular platforms

While Flowise is a popular alternative, Dify also competes with other no-code AI platforms:

Dify vs. LangFlow:

• Dify focuses on complete app deployment, while LangFlow emphasizes experimentation
• Dify includes built-in hosting, LangFlow requires separate deployment

Dify vs. Bubble + AI plugins:

• Dify is AI-native from the ground up
• Bubble requires multiple plugins for similar AI functionality

Dify vs. Zapier/Make + AI:

• Dify is designed specifically for conversational AI
• Zapier/Make better for general automation workflows

Now, let’s go through some best practices for using Dify efficiently.

Best practices for using Dify

Apply these best practices to make the most out of Dify:

• Structure knowledge sources: Upload well-formatted documents with clear headings and context for better response accuracy.
• Use prompt engineering: Experiment with different prompt styles to guide the bot’s behavior effectively.
• Test iteratively: Use the chat preview often to catch logical gaps or knowledge errors early.
• Monitor analytics: Leverage Dify’s logs and feedback tools to refine your app continuously.
• Secure your endpoints: If deploying via API, add authentication and rate-limiting.

Following these best practices will ensure effective usage of Dify.ai.

Conclusion

In this tutorial, we discussed how to build a full-fledged, AI-powered Q&A bot using Dify. We covered what Dify.ai is, why we should use it, how it compares to Flowise, and some best practices for using it efficiently.

Dify is more than just a chatbot builder — it’s a platform that enables us to create AI-native apps without the need for coding. Its simplicity, flexibility, and power make it an ideal choice for teams of all sizes.

If you want to learn more about generative AI, check out the Intro to Generative AI course on Codecademy.

Frequently asked questions

1. Is Dify free to use?

Yes, Dify offers a free self-hosted version. There is also a cloud-hosted version available with pricing plans based on usage and features.

2. Is Dify open-source?

Absolutely. Dify is open-source and available on GitHub, allowing developers to customize and make contributions to the platform.

3. What is the difference between n8n and Dify?

While n8n is focused on general workflow automation (like Zapier), Dify is designed specifically for building AI-native applications. Though they serve different purposes, they can complement each other.

4. How to create a workspace in Dify?

After signing in to Dify, you can click on Create Workspace, name it, and start adding your apps, datasets, and configurations.

5. How does Dify work?

Dify connects with LLM providers like OpenAI or Anthropic and lets you design AI apps visually. It retrieves information from knowledge bases and handles user interactions using prompt workflows.

6. How do you create knowledge in Dify?

You can upload documents (PDFs, Word, text files) or import content from web URLs. Dify automatically parses and indexes this data, turning it into a retrievable knowledge base for your app.