# Dify: A No-Code AI Application Development Platform

> Build your AI applications visually

Hello, I'm Yupi.

In previous articles, we learned about no-code platforms like Bolt.new and Baidu Miaoda for quickly generating websites and applications. But what if you want to create AI applications, such as intelligent customer service, knowledge base Q&A, or AI assistants? What tools should you use?

In this article, I'll introduce **Dify**, a no-code platform specifically designed for developing AI applications. Through a visual interface, you can quickly build powerful AI applications without writing any code.

Let me walk you through how to use Dify with practical examples, while also explaining some core AI concepts along the way.

## 1. What is Dify?

[Dify](https://dify.ai/) is an open-source AI application development platform that allows you to build AI applications visually.

How is it different from Bolt.new?

Bolt.new is mainly used to generate regular websites and applications, such as personal homepages or e-commerce sites. Dify, on the other hand, specializes in AI applications, including:

- Intelligent customer service chatbots
- Knowledge base Q&A systems
- AI writing assistants
- Document analysis tools
- AI workflow automation

Dify provides a visual configuration interface where you can build AI workflows through drag-and-drop, configure large models, set prompts, add knowledge bases, and more—all without writing code to create powerful AI applications.

![](https://pic.yupi.icu/1/853427c8123decb5ea3d163ae3bb8ab635d95e92f7ee14a2e51e54df06e94fd8.png)

## 2. Getting Started with Dify Quickly

Let me guide you through a practical example to quickly get started with Dify.

### 1. Create an AI Application

First, go to the [Dify platform](https://dify.ai/), register an account, and log in. Then create an AI application and enter the AI conversation interface.

![](https://pic.yupi.icu/1/1743560753186-1e9452e6-0d38-4070-b369-c674bc418c91.png)

### 2. Choose a Large Model

For first-time use, we need to select a **large model (LLM)**.

**Large models are the brains of AI**, referring to artificial intelligence models with massive parameters that acquire extensive knowledge and capabilities through large-scale pre-training.

![](https://pic.yupi.icu/1/1743560803824-ab33d9d9-e994-45e5-8190-fc104e679747.png)

Different large models vary in parameter size, processing power, and supported conversation lengths. For example, Claude Opus 4.5 excels in programming, Gemini 3 Pro supports ultra-long contexts, and DeepSeek is completely free.

![](https://pic.yupi.icu/1/1743560841202-c37cde5b-0b25-4ebb-adff-3ab66af35d75.png)

After selecting a large model, we can adjust its output by setting parameters. For example, **temperature** controls the randomness of the model's output:

- Higher temperature values result in more random and diverse outputs (suitable for creative writing)
- Lower temperature values produce more deterministic and conservative outputs (suitable for professional Q&A)

![](https://pic.yupi.icu/1/1743560855583-7efaebb7-3552-4a5b-9787-adbb9acaddc6.png)

### 3. Set Prompts

Next, we'll engage in a conversation with the AI. The input we provide to the AI is called a **prompt**, which guides the model to generate specific content or perform certain tasks.

The quality of the prompt directly determines the accuracy of the AI's output. Prompts can be divided into two types:

- System prompts: Overall constraints on the AI's output, set in advance
- User prompts: Content input by users on the fly

![](https://pic.yupi.icu/1/1743560920031-d86572e4-b09e-46b4-8aa8-c734a96bec44.png)

For example, if I want to create a programming assistant, I can set the system prompt as:

```
You are a professional programming assistant proficient in Python, JavaScript, Java, and other languages.
Provide concise and clear answers with code examples.
```

Then users can directly ask: "How to read a file in Python?"

### 4. Understanding Tokens

After a conversation, you'll notice "Token cost" displayed below the dialogue.

![](https://pic.yupi.icu/1/1743561058442-beebd2ac-94a0-4f00-8e56-f819822247e1.png)

Seeing "cost" might make some nervous—what are Tokens? Are they expensive?

**Tokens are the basic units of text processed by large language models**, which could be words or punctuation marks. Both input and output are calculated in Tokens. Generally, more Tokens mean higher costs and slower output speeds.

Different models have varying pricing, typically costing tens of dollars per million Tokens. You can use online Token calculators to estimate costs.

![](https://pic.yupi.icu/1/1743561097206-472514a9-3d13-4408-b222-2207b00f611a.png)

But don't worry too much—daily usage costs are usually low, and many platforms offer free quotas.

### 5. Add a Knowledge Base (RAG)

Sometimes, large models may lack certain information. For example, if you ask AI to summarize [Yupi's "Ultimate Resume Writing Guide"](https://www.codefather.cn/course/cv), the information might be inaccurate because the AI hasn't read the article.

In such cases, we can enable the knowledge base feature, which uses **RAG (Retrieval-Augmented Generation)** technology to supplement the AI's knowledge with external sources.

![](https://pic.yupi.icu/1/1743561648847-337df359-2e2a-4e05-bec6-fdff52b3be1d.png)

First, create a knowledge base and upload documents:

![](https://pic.yupi.icu/1/1743561783744-1ddce7bb-802e-4feb-9e8f-7e0a83b4ad98.png)

Then segment the text, setting your own chunking rules:

![](https://pic.yupi.icu/1/1743561816205-22494e52-c011-49fe-8537-3b7f0f441a51.png)

Next, use **Embedding** technology to convert text into vector representations and store them in a vector database.

When a user asks a question, the question is converted into a vector, and relevant information is retrieved from the knowledge base. This information, along with the question, is then fed into the large model for processing, making the AI's answers more accurate.

![](https://pic.yupi.icu/1/1743561872916-7971c368-14bd-49c2-9bd9-604973f469e3.png)

This way, the AI can answer questions based on your provided knowledge base.

### 6. Publish and Call

Now, our AI application is complete. You can publish it for others to use or call it via **API interfaces** in your own code programs through network requests.

![](https://pic.yupi.icu/1/1743561915955-ad27735a-c927-4207-b769-03fda32081b6.png)

## 3. AI Agents and Workflows

So far, we've only created a simple chat assistant. But Dify also supports more powerful features—**AI agents**.

Agents are AI systems that can perceive environments, reason, plan, make decisions, and autonomously take actions to achieve goals.

![](https://pic.yupi.icu/1/1743561972671-9c7ad13e-a467-4a08-ba14-711d4640939c.png)

We can provide agents with **tools**, such as web search, weather queries, database calls, etc., enabling them to perform more complex tasks.

After installing tools, the agent will use them when needed. For example, it might retrieve content from the web, summarize it, and then reply. This expands the AI's application scope and capabilities infinitely.

![](https://pic.yupi.icu/1/1743562005435-e5ece3f2-5f4b-4729-b490-a1e51f1f006e.png)

Of course, if your AI model isn't smart enough, it might not use tools effectively. I recommend using more capable reasoning models for agents.

Some models employ **Chain-of-Thought (CoT)** and **ReAct** techniques, where the model first thinks about the problem, analyzes it, proposes an action plan, acts, and then further reasons based on results. The intermediate steps and reasoning processes are visible, helping us understand how the model reaches conclusions.

![](https://pic.yupi.icu/1/1743562152661-80fabf5f-07a4-4463-a980-67da980f0ede.png)

Sometimes, a single agent can't complete tasks like automatically generating 100 short videos or creating and publishing a game. 

In such cases, we can use **agent workflows** (Agentic Workflow), which allow agents to combine functions through planning and orchestration, automating complex tasks—similar to visual programming.

![](https://pic.yupi.icu/1/1743562195750-57a3b344-4282-4279-bd71-510f60fc17c6.png)

## 4. MCP Service Integration

Finally, let's discuss a trending concept: **MCP (Model Context Protocol)**, a standardized protocol for AI interactions with external tools or data.

![](https://pic.yupi.icu/1/1743562215479-a19f8b1c-0190-41b4-8a2f-f508b24e74a7.png)

Simply put, MCP services make it easier to integrate various tools and data into AI, enhancing application capabilities.

First, install the MCP Agent policy to enable agents to call MCP:

![](https://pic.yupi.icu/1/1743562275496-34bcb486-235d-4d97-bc5a-cdf00f59cff7.png)

Then, visit the [MCP Directory](https://mcp.so/) to find needed MCP services, such as querying the current time.

![](https://pic.yupi.icu/1/1743562325916-dbef66dc-d0d1-4a60-9bed-68691c462677.png)

Back in the agent workflow, fill in the MCP server address, invocation commands, query conditions, etc. The AI can then send requests to MCP to fetch data when needed.

![](https://pic.yupi.icu/1/1743562400230-79c99317-98f1-4579-8884-a5bf53623683.png)

## 5. Other AI Application Development Platforms

Besides Dify, here are some other notable AI application development platforms.

### Coze

[Coze](https://www.coze.com/) is an AI application development platform by ByteDance, offering numerous plugins for easy app development.

Coze's strengths are no-code, visual workflows, and many pre-built plugins and templates, making it quick to learn. Ideal for individuals and lightweight applications.

### Alibaba Cloud Bailian

[Alibaba Cloud Bailian](https://bailian.console.aliyun.com/) is an enterprise-grade AI application development platform supporting RAG knowledge bases, workflow orchestration, and more.

Bailian's strengths are enterprise-level capabilities, visual workflow orchestration, and deep integration with other Alibaba Cloud services, making it suitable for businesses.

### How to Choose?

If you're an individual developer or lack coding experience and want to quickly build an AI application, Dify and Coze are great choices.

For enterprise users or Java developers needing stability and enterprise features, consider Alibaba Cloud Bailian.

I primarily use Alibaba Cloud Bailian because, as a full-stack Java developer, Alibaba's dominance in China's Java ecosystem is unmatched. Their Spring AI Alibaba framework integrates seamlessly with their AI services, enabling rapid development of complete AI applications.

## 6. Dify Practical Tips

Here are some practical tips I've gathered while using Dify.

1. Choose the right large model  
   Different tasks suit different models. For creative writing, GPT-4 or Claude; for code generation, Claude excels; for budget constraints, DeepSeek or Gemini Flash.

2. Optimize prompts  
   Prompt quality directly affects AI output. Recommendations:
   - Define roles clearly (e.g., "You are a professional...")
   - Specify task requirements clearly
   - Provide concrete examples
   - Set output formats

3. Leverage knowledge bases  
   If your AI application needs to answer questions based on specific knowledge, use the knowledge base feature. Upload relevant documents to ensure accurate answers.

4. Test and iterate  
   After building an AI application, test extensively. Try various questions and adjust prompts or knowledge bases based on results.

5. Use workflows  
   For complex tasks, use workflows. Break tasks into steps, each handling a subtask, then combine them. This improves control and debugging.

## Final Thoughts

By now, you should have a basic understanding of Dify and AI application development.

**Building AI applications with Dify is truly simple.** No coding is needed—just configure settings to create powerful AI applications.

Through using Dify, you'll also grasp core AI concepts like large models, prompts, Tokens, RAG, and agents. These concepts are useful not just in Dify but across other AI tools.

I recommend trying Dify to build a simple AI application, such as a programming Q&A assistant, document summarizer, or knowledge base Q&A system. You'll soon discover the joy of AI development.

## Recommended Resources

1) Yupi's AI Navigation Site: [AI resource directory, latest AI news, free AI tutorials](https://ai.codefather.cn)  
2) Programming Navigation Learning Circle: [Learning paths, tutorials, projects, job guides, Q&A](https://www.codefather.cn)  
3) Programmer Interview Cheatsheets: [Internship/campus/social recruitment FAQs, company interview questions](https://www.mianshiya.com)  
4) Programmer Resume Builder: [Professional templates, rich examples, interview prep](https://www.laoyujianli.com)  
5) 1-on-1 Mock Interviews: [Essential for internship/campus/social recruitment offers](https://ai.mianshiya.com)