Ordinary programmers vs. prompt word experts: The same AI, but the gap is 10 times!
Mastering AI prompt words allows ordinary programmers to become AI experts!
Core content:
1. The definition of AI prompt words and their key role in user-AI interaction
2. The importance of learning prompt words: improving accuracy and saving time costs
3. Specific examples in daily scenarios and AI programming scenarios to show how to design effective prompt words
1. What are AI prompt words?
AI prompts are instructions, descriptions or questions that users enter when interacting with artificial intelligence models (such as ChatGPT, Doubao, DeepSeek, Wenxinyiyan, etc.), which are used to guide the AI model to generate content that meets expectations. Simply put, prompts are the "bridge" for you to communicate with AI, which directly determines the direction, quality and efficiency of AI output content.
The basic characteristics of prompt words:
Clarity : Help AI understand user needs accurately through clear and unambiguous instructions.
Structuring : Through the design of structured prompt words, the quality and usability of AI-generated results can be significantly improved.
Flexibility : Users can adjust prompt words at any time according to specific needs to achieve different output effects.
2. Why learn prompt words?
Whether you are a beginner or an experienced developer, mastering prompt design is a key skill for using AI tools efficiently. A good prompt can not only improve AI's understanding ability, but also significantly improve the quality and efficiency of generated content.
Specifically, the importance of learning prompt word design is mainly reflected in the following aspects:
Improve accuracy
Ambiguous or incomplete instructions can cause AI's output to deviate from expectations or even go completely off topic. Clear and specific prompts can help AI better understand task requirements and output content that better meets actual needs.
Examples of everyday scenarios:
❌ Ambiguous command: "Write a travel guide."
✅ Explicit instructions: "Write a budget travel guide for college students in Europe, including transportation, accommodation, and money-saving tips."
AI programming scenario examples:
❌ Ambiguous instructions: "Write me a sorting code."
✅ Clear instructions: "Write a quick sort algorithm in Python with well-commented code, O(n log n) time complexity, and example input and output."
Save time cost (Time Efficiency)
By optimizing the prompt words, the trial-and-error process of repeated communication and adjustment with AI can be reduced. Clear prompt words can accurately express requirements at one time, avoid repeated modifications due to misunderstandings, and greatly improve work efficiency.
Example scenario:
❌ Ambiguous: "Help me optimize this code."
✅ Be clear and specific: "Optimize the following JavaScript code to reduce page load time and the number of DOM operations, and provide optimized code examples and performance analysis."
Improve code quality
Efficient prompt word design can significantly improve the quality of AI-generated code, reduce manual coding and debugging time, and help developers obtain high-quality code output faster.
Example scenario:
❌ Blur: "Write a login function."
✅ Be specific: "Implement a login component using React Hooks and TypeScript, requiring integration of JWT authentication, including input validation and error handling, the code complies with the ESLint specification, and provides unit test cases."
Improve communication efficiency
Good prompt word design skills can help you communicate with AI more smoothly, reduce misunderstandings and rework, and improve overall communication efficiency.
Example scenario:
❌ Blur: "Help me look at this bug."
✅ Be specific: "The following code causes a stack overflow exception when executed. Please analyze it as a senior Java development engineer and give specific reasons and repair suggestions."
Promoting Deep Understanding
The process of learning and practicing prompt word design is itself a process of in-depth thinking about the problem, which helps users understand the essence of the problem more clearly and cultivate structured thinking ability.
Example scenario:
When designing prompts, users need to clarify the task background, technical details, and expected goals. This process can help users analyze the problem in depth and clarify their thoughts.
Learning to design prompt words is not only a necessary skill for using AI tools efficiently, but also an important method to improve personal structured expression and problem analysis capabilities. By mastering the skills of prompt word design, you can better unleash the potential of AI, improve work efficiency and output quality, and ultimately achieve the best results of human-machine collaboration.
3. Basic structure of prompt words
A complete and efficient prompt usually consists of multiple structural elements. Each element can help AI understand and perform specific tasks more accurately, improving the quality and efficiency of generated results.
The following are the six core elements of the prompt word:
| Element | Definition | Example | Best Practices |
|---|---|---|---|
| 1. Instruction | |||
| 2. Context | |||
| 3. Input Data | |||
| 4. Role | |||
| 5. Output Format | |||
| 6. Expectation |
The following is a complete example showing how to combine the above six elements to design efficient and structured prompt words:
Instruction
Please develop a React component for the shopping cart page of the e-commerce platform to realize the dynamic increase and decrease function of the number of goods.
Context
- Technology stack: Use React 18, TypeScript 5.0, and Ant Design 5.9.0.
- Problem description: Currently, the shopping cart page is implemented using class components. The code is complex and difficult to maintain. We hope to refactor it into a function component and use hooks for optimization.
- Business scenario: The shopping cart page of the e-commerce platform needs to improve the user interaction experience.
Input Data
- Existing code repository link: https://github.com/example/cart-page
- Product data format example:
{
"id": "12345",
"name": "Wireless Headphones",
"price": 299,
"quantity": 2
}
Role
Please complete this task as a senior front-end architect.
Output Format
- Implemented using function components and React Hooks, in line with TypeScript best practices.
- The output code must comply with the ESLint specification and come with detailed comments.
- Provide complete code and usage documentation using Markdown format.
Expectation
- Component implementation needs to consider performance optimization and avoid unnecessary rendering.
- Provide unit test code examples to ensure the reliability of components.
Through the above structured prompt word design, the accuracy of AI understanding tasks can be significantly improved, the cost of repeated communication can be reduced, and the generated code or solution can be ensured to be more in line with actual needs and meet high quality requirements.
4. Tips for designing AI programming prompts
With the widespread use of AI programming assistants, the reasonable design of prompts can greatly improve communication efficiency and obtain higher-quality code output. The following details how to optimize the design of prompts from the two dimensions of design principles and structured framework.
Design principles
When designing prompt words, the following three basic principles should be followed:
Divide-and-Conquer
When faced with complex requirements, break the problem down into multiple clear and specific subtasks and proceed step by step.
Primary question (counterexample):
Help me write a crawler in Python
Optimized questions (positive example):
Step 1: Identify requirements and tools
I need to use Python to crawl Zhihu hot list data. Please recommend a suitable library and explain the reason for the recommendation.
Step 2: Basic code framework
Based on the library I choose, a basic code framework is given, including request sending, data parsing and data saving functions.
Step 3: Complete the code details
Please add anti-crawler mechanisms (such as random request headers, IP proxies) and exception handling in the basic framework to ensure stable operation of the program.
Advantages: Gradually guide AI to understand requirements, reduce communication costs, and improve code quality.
Clarity & Completeness
Provide clear, complete, and specific contextual information to avoid vague and one-sided descriptions, so that AI can accurately understand the problem.
Wrong example (counterexample):
My code reports an error when running, what should I do?
Optimization example (positive example):
`IndexError: list index out of range` occurs when executing the following Python code . The input data is [1,3,5]. Please analyze the cause and provide a repair solution:
def get_second_element(lst):
return lst[2]
Advantages: Provides clear context (error type, input data, code snippet) to help AI quickly locate problems and provide accurate solutions.
Knowledge Alignment
Clearly inform the AI of the technology stack, language, framework, and version used to avoid additional communication costs caused by technology mismatch. At the same time, provide reference resources such as official document links, technical blogs, or code repository addresses to help the AI understand the demand background and technical details more accurately.
Example (positive example):
Use React 18 to develop a draggable table component, requiring compatibility with TypeScript 5.0, and use Ant Design 5.9.0 as the UI library.
Related reference resources:
React official documentation: https://react.dev/
Ant Design component library documentation: https://ant.design/components/table-cn
Drag and drop function reference implementation: https://github.com/react-dnd/react-dnd
Advantages:
Reduce communication costs and ensure that the code output by AI is seamlessly integrated with the existing project technology stack.
Provide authoritative and accurate reference information to help AI better understand the demand background, quickly locate technical key points, and improve the quality and accuracy of generated results.
Structural Framework
The structured prompt word framework can help AI understand requirements more efficiently. Commonly used frameworks include ICIO, CRISPE, and BRTR:
ICIO Framework (Instruction-Context-Input-Output)
Example (positive example):
Instruction: Implement JWT user authentication
Context: Web project based on Spring Boot 3.2
Input: Existing User entity class fields (id, username, password, role)
Output: Provides complete Java code, including Token generation, verification, and permission interceptor implementation.
CRISPE Framework (Context-Request-Information-Specifics-Purpose-Expectation)
Example (positive example):
Context: The existing Java server code has a memory leak problem
Request: Help locate and fix this issue
Information: Provides a heap memory dump file (heapdump.hprof)
Specifics: Use the Eclipse Memory Analyzer tool for analysis
Purpose: Ensure long-term stable operation of the server
Expectation: Outputs the specific leak class name, cause analysis, and repair code examples.
BRTR Framework (Background-Role-Task-Response)
Example (positive example):
Background: Frequent timeout issues occur when calling between microservices
Role: Senior distributed system architect
Task: Design and implement a circuit breaker mechanism
Response: Provides a configuration example of the circuit breaker mechanism based on Spring Cloud Hystrix, and briefly describes the role of the configuration parameters.
Reasonable design of AI programming prompts, following the principles of step-by-step decomposition, precise description and knowledge alignment, combined with structured frameworks (ICIO, CRISPE, BRTR) and other optimization techniques can significantly improve the accuracy of AI understanding requirements and output quality.
5. Practical Examples of Programming Scenarios
In the field of programming, the key to using AI tools efficiently is to carefully design prompts to clarify requirements, provide background information, and effectively control output effects. The following practical examples show how to write high-quality prompts suitable for programming scenarios.
1. Code generation prompt word template
When using AI for code generation, it is recommended to use a structured prompt word template to ensure that the generated results are accurate, standardized, and easy to maintain:
#Character Setting
You are a [language/framework] developer with [X years of experience] and familiarity with [specific domain or technical details].
#Feature Request
Please develop a [specific functional description], the specific requirements are as follows:
-Technology stack: [language/framework and version]
- Input: [Data type/example description]
- Output: [Expected output format/performance indicators]
-Constraints : [security requirements/compatibility requirements/performance requirements]
#Coding Standards
- Code style: [such as compliance with PEP8, Google Java coding standards, etc.]
- Comment requirements: [Detailed comments, function description, parameter description, etc.]
-Test requirements: [whether unit testing is required, test coverage, etc.]
#Example (Python data cleaning)
As a Python data analysis expert, please write a data cleaning function:
- ** Input ** : Pandas DataFrame with missing values, for example:
import pandas as pd
df = pd.DataFrame({
'Age': [25, None, 30, 22, None],
'Gender': ['Male', 'Female', None, 'Female', 'Male'],
'Income': [5000, 6000, None, 4500, 5200]
})
- ** Output ** : Automatically identify numeric and categorical fields, fill missing values and return DataFrame and log records.
- Numeric fields are filled with the median;
- The categorical fields are filled with the majority;
-Returns the processed DataFrame and detailed modification log.
- ** Constraints ** :
-The code needs to run efficiently and perform well when processing millions of data;
- The code must comply with PEP8 specifications and come with detailed comments;
-Use the latest stable version of Pandas.
(II) Code debugging prompt strategy
In daily development, when faced with code anomalies or optimization requirements, precise prompt words can help AI quickly locate problems and provide effective solutions. Common code debugging prompt strategies include:
Sandwich feedback method (problem location and repair)
Describing problems in a structured way helps AI quickly understand the context and accurately locate errors:
【Error description】
A NullPointerException occurs when calling the userService.findById() method.
Provide context
-Call parameter: id = 358 (the database confirms that the record exists)
-Exception stack information:
java.lang.NullPointerException: Cannot invoke "com.example.User.getName()" because "user" is null
at com.example.controller.UserController.getUserInfo(UserController.java:48)
...
Request a solution
Please analyze the possible causes of this exception and provide specific repair solutions and optimization suggestions.
Comparative analysis (code optimization and evaluation)
By comparing different implementation methods, we can help AI come up with better solutions:
【Comparative analysis requirements】
The following two Kotlin coroutine codes are both used to process network requests. Please analyze and compare them:
- Code segment A:
kotlin
viewModelScope.launch {
val result = withContext(Dispatchers.IO) { apiService.getData() }
updateUI(result)
}
- Code segment B:
kotlin
viewModelScope.launch(Dispatchers.IO) {
val result = apiService.getData()
withContext(Dispatchers.Main) {
updateUI(result)
}
}
Request a solution
Please analyze the advantages and disadvantages of the above two implementation methods, recommend a better implementation method, and give specific reasons.
Scenario Reproduction Method (Bug Reproduction and Analysis)
Help AI quickly understand and locate complex problems by providing detailed reproducible steps:
Bug description
In the front-end React project, a cross-domain (CORS) error occurs when using Axios to request the back-end API.
【Steps to reproduce】
1. The front-end React project runs on localhost:3000;
2. The backend Spring Boot API service runs on localhost:8080;
3. The front end uses Axios to initiate a GET request to http://localhost:8080/api/users;
4. Browser console error:
Access to XMLHttpRequest at 'http://localhost:8080/api/users' from origin 'http://localhost:3000' has been blocked by CORS policy.
Request a solution
Please analyze the specific reasons that lead to this cross-domain error and provide specific front-end and back-end repair solutions (with code examples).
Through the above programming scenario examples, we can see that good prompt word design can not only improve the accuracy and quality of the code generated by AI tools, but also effectively save developers' time costs and significantly improve development efficiency. Mastering and skillfully applying these prompt word design strategies can help developers maximize the potential of AI tools and achieve efficient human-computer collaboration.
6. Common Misunderstandings and Solutions
In programming tasks, it is crucial to clarify requirements and constraints. Here are some common misunderstandings and corresponding optimization solutions:
| Types of Misconceptions | Programming scenario examples | Optimization plan |
|---|---|---|
| Over-abstraction | ||
| Lack of constraints | ||
| Ignore exceptions | ||
| Character out of focus | ||
| Unclear goals | ||
| Missing context | ||
| Incomplete requirements | ||
| Ignoring maintainability |
By identifying and avoiding these pitfalls, you can complete your programming tasks more efficiently while improving the quality and usability of your code.
Other learning materials
Learning AI programming requires not only practice, but also the use of rich learning resources to broaden your horizons and improve your abilities. The following are some high-quality learning materials and tool recommendations, covering prompt word design, AI programming skills, and large model-related knowledge, to help you quickly get started and deeply master AI programming technology:
LangGPT
Introduction : LangGPT is an open source project that focuses on optimizing the application and development of large language models (LLMs) through structured prompt word design and template methods. It not only provides rich examples and code frameworks, but also introduces the concept of systematic prompt word programming language to help developers quickly build efficient AI applications.
Applicable people : technicians and developers who are interested in large language model (LLM) development, and users who want to optimize prompt word design to improve model performance.
Link : LangGPT GitHub project
Recommended reason : LangGPT provides a complete development process from basic to advanced, combined with structured prompt word design and template method, suitable for learning how to efficiently apply language models to practical scenarios. At the same time, the project also supports prompt word optimization for multimodal models (such as GPT-4O), further expanding its application potential.
The Road to AGI
Introduction : A learning platform focusing on prompt word design and AI application, providing a large number of practical prompt word examples and design skills to help users use large language models more efficiently.
Applicable people : Users who want to improve their prompt word design ability.
Link : The Road to AGI
Recommended reason : The content covers prompt word design for multiple scenarios, with detailed cases from basic to advanced. It is a high-quality resource for learning Prompt Engineering.
AI Short
Introduction : An online platform focusing on AI tools and prompt word optimization, providing a wealth of prompt word templates and practical skills, suitable for individual and corporate users to quickly get started with AI tools.
Applicable people : Users who have actual needs for AI tools, especially those who want to improve work efficiency.
Link : AI Short
Recommended reason : The platform content is updated in a timely manner, covering the use of AI in various scenarios, and is a rare good helper in daily work.
DeepSeek Prompt Dictionary
Introduction : DeepSeek provides a comprehensive prompt word library that covers application scenarios in multiple fields, with detailed prompt word templates from text generation to data analysis.
Applicable people : developers and users who need to quickly call prompt words in specific scenarios.
Link : DeepSeek Prompt Dictionary
Recommended reason : The content is clearly categorized and covers a wide range, making it an excellent reference for prompt word design.
Tongyi Qianwen Tips Dictionary
Introduction : The prompt word library launched by Alibaba Cloud focuses on the application of the Tongyi Qianwen large model and provides a wealth of prompt word design cases and usage guidelines.
Applicable people : Developers and enterprise users who want to gain a deeper understanding of the Tongyi Qianwen model.
Link : Tongyi Qianwen Tips Dictionary
Recommended reason : Official resources, authoritative and practical content, suitable for users who are interested in Tongyi Qianwen.
Claude Prompt Dictionary
Introduction : The Claude prompt word library provided by Anthropic focuses on the application of the Claude model and provides prompt word design references in various scenarios.
Applicable people : Users who want to learn the design of Claude model prompt words.
Link : Claude Tips Dictionary
Recommended reason : The content is detailed and covers various application scenarios of the Claude model. It is a high-quality resource for learning the Claude model.
OpenAI official "Prompt Word Engineering Course" super detailed Chinese notes
Introduction : This is the Chinese version of the Prompt Engineering course officially launched by OpenAI, which records in detail the core methods and practical techniques of prompt word design.
Applicable people : Users who want to systematically learn prompt word design.
Link : OpenAI official "Prompt Word Engineering Course" super detailed Chinese notes
Recommended reason : Translated and compiled from the official course, the content is authoritative and comprehensive, very suitable for beginners and advanced users to learn in depth.
The Large Model Cookbook for Developers - LLM Cookbook
Introduction : This open source manual launched by the Datawhale team focuses on the development and application of large language models (LLMs), covering model principles, prompt word design, code implementation and other aspects.
Applicable people : technicians and researchers who want to systematically learn the development of large language models.
Link : LLM Cookbook GitHub Project
Recommended reason : The content is detailed and contains a large number of practical cases and code examples. It is a valuable resource for developers to learn LLM.
