A complete collection of tips, templates and techniques for developing intelligent application

Written by
Iris Vance
Updated on:July-14th-2025
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Master the art of communicating with AI agents and improve the efficiency and accuracy of AI application development.

Core content:
1. The basic concept and importance of prompt engineering
2. How prompt engineering can eliminate ambiguity, improve efficiency and controllability
3. Core prompt template skills, including application examples of role definition templates

Yang Fangxian
Founder of 53AI/Most Valuable Expert of Tencent Cloud (TVP)

Imagine standing in front of an alien and trying to explain to it how to make a perfect cup of coffee. It has superb execution capabilities, but knows nothing about Earth. How would you phrase it? Would you simply say "make me a cup of coffee" or provide a detailed set of step-by-step instructions? This is exactly the challenge we face when communicating with AI agents. 

 

In today's era of rapid development of AI, large language models (LLMs) such as GPT-4 and Claude have become the infrastructure for building intelligent applications. However, in order for these powerful models to truly understand our intentions and work the way we expect, it is crucial to master efficient prompt skills. 

 

This article will explore in depth the prompt word template techniques in intelligent agent application development to help developers build smarter and more accurate AI applications. 

 

Basic concepts of prompt word engineering

 

What is the prompt word project?

Prompt Engineering refers to the process of designing, optimizing, and implementing prompts input to AI models to guide the models to generate the desired outputs. It is the bridge of communication between humans and AI, and determines whether AI can accurately understand our needs. 

Imagine the prompt word is a key, and the AI ​​model is a treasure chest with infinite possibilities. The right key can open a specific lock and get the exact treasure; while an ambiguous key may lead to opening the wrong lock or not opening the treasure chest at all. 


Why is prompt word engineering so important?

  1. 1.  Disambiguation : Clear cues can reduce AI "hallucinations" and misunderstandings

  2. 2.  Improve efficiency : Good prompts can reduce the number of back-and-forth communications

  3. 3.  Enhanced controllability : Detailed instructions allow AI to work as expected

  4. 4.  Reduce costs : Accurate prompts can reduce unnecessary token consumption

 

Core prompt word template skills

1. Role definition template

Giving AI a well-defined role can significantly improve its performance in a specific domain. 

You are an expert in {field of expertise} with {years} of experience, specializing in {specific skill}.
Please help me with {specific task}.

Example application : 

You are a software architect with 15 years of experience in cloud-native application development, specializing in microservice architecture and DevOps practices.
Please help me design the backend system architecture of an e-commerce platform, which requires high availability, scalability, and takes into account traffic peak processing.

This template activates the model's knowledge about specific fields by clarifying the AI's "identity", making the answers more professional and in-depth. 

2. Step decomposition template

For complex tasks, breaking them down into clear steps can result in a more structured output. 

Please follow these steps to complete {task}:
1. First, {Specific requirements for the first step}
2. Then, {Specific requirements for the second step}
3. Next, {Specific requirements for the third step}
...
N. Finally, {the last step is specific requirements}

The output format of each step is: {define output format}

Example application : 

Please follow the steps below to analyze this Python code:
1. First, explain the overall function and purpose of the code
2. Then, identify the key variables and functions in the code
3. Next, point out possible performance issues or bugs in the code
4. Finally, provide optimization suggestions

The output format for each step is: ## [step name] followed by a detailed analysis

3. Output format control template

Explicitly specifying the output format allows AI to generate content that is easier to process. 

Please answer the following questions in {format type} format: {question content}

Output requirements:
- Format: {Detailed format description}
- Length: {word count or paragraph requirement}
- Style: {Style Required}
- Others: {other special requirements}

Example application : 

Please answer the following questions in JSON format: What are the main advantages and challenges of microservice architecture?

Output requirements:
- Format: Standard JSON, with two primary keys "advantages" and "challenges", each of which contains an array of objects containing "title" and "description"
- Length: Each description should be no less than 50 words
- Style: Technical Professional Style
- Others: Ensure that the JSON format is valid and can be parsed directly

 

Advanced Cue Word Techniques

1. Chain of Thought Tips

By guiding AI to reveal its thought process, more reliable and transparent results can be achieved. 

Please solve the following problem: {Complex problem}
Please think step by step, show your reasoning process, and then give your final answer.

Example application : 

# Implementing thought chain prompts in code
prompt =  """
Please analyze the conversion rate data of the following e-commerce websites and make suggestions for improvement:
- Website visits: 10,000/day
- Product page views: 6,000/day
- Add to cart: 1,200/day
- Start checkout: 800/day
- Completed purchase: 400/day

Please think through the steps, show your reasoning, and then give a final suggestion for improvement.
"""


response = ai_model.generate(prompt)

2. Multi-angle analysis template

Guide AI to think about problems from different angles and obtain more comprehensive analysis. 

Please analyze {topic} from the following perspectives:
1. {Angle 1} perspective
2. {Angle 2} perspective
3. {Angle 3} Perspective
...

For each angle, provide:
- Main points
- Supporting evidence
- Potential Problems

Example application : 

Please analyze "Introducing ChatGPT-type AI tools in enterprises" from the following perspectives:
1. From the perspective of productivity improvement
2. Information security perspective
3. Employee skills development perspective
4. Cost-effectiveness perspective

For each angle, provide:
- Main points
- Supporting evidence
- Potential Problems

3. Example driver template

Guide the AI ​​to understand the desired output style and content by providing specific examples. 

I need you to help me {task description}.

Here is an example of the output I expect:
Example 1: {Detailed Example 1}
Example 2: {Detailed Example 2}

Now, please generate output for {specific input} in a similar format.

Example application : 

I need you to help me explain technical concepts in a way that is understandable to non-technical people.

Here is an example of the output I expect:
Example 1: What is an API?
An API is like a waiter in a restaurant, it receives your request (order), communicates the request to the kitchen (server), and then sends the result (food) back to you. You don't need to know how the kitchen cooks the food, you just need to know how to place an order with the waiter.

Example 2: What is cloud computing?
Cloud computing is like using the public power grid instead of generating your own electricity. Instead of purchasing and maintaining your own generators (servers), you purchase electricity (computing resources) from the grid (cloud service provider) on demand. This is both economical and convenient.

Now, please explain "Blockchain Technology" in a similar format.

 

Practical application examples of prompt word templates

 

Case 1: Intelligent Customer Service Robot

Build a customer service bot that can handle a variety of customer queries: 

def  customer_service_bot (query, customer_info):
    prompt =  f"""
    You are a professional e-commerce customer service representative named AI Assistant. Your task is to help customers solve problems and provide support.
    
    Customer Information:
    - Name: {customer_info[ 'name' ]}
    - Membership level: {customer_info[ 'membership_level' ]}
    - Purchase history: {customer_info[ 'purchase_history' ]}
    
    Please follow the steps below to respond to customer inquiries:
    1. First, address the customer by name and show that you understand their problem
    2. Then, answer the customer's questions directly, providing accurate, relevant information
    3. If you need more information, ask politely
    4. Finally, ask if there is anything else you can help with
    
    Customer query: {query}
    
    Response format:
    Greetings: [Personalized Greetings]
    Answer: [Direct response to the query]
    Closing: [Friendly closing and follow-up service tips]
    """

    
    return ai_model.generate(prompt)

 

Case 2: Code Review Assistant

 

Building an AI assistant that can automatically review code: 

def  code_review_assistant (code_snippet, language):
    prompt =  f"""
    You are an experienced {language} development expert with a focus on code quality and best practices. Please conduct a comprehensive review of the following code:
    
    ``` {language}
    {code_snippet}
    ```
    
    Please analyze from the following aspects:
    1. Code function: What is the main function of this code?
    2. Code quality: evaluate the readability, maintainability and efficiency of the code
    3. Potential issues: Identify possible bugs, security vulnerabilities, or performance bottlenecks
    4. Improvement suggestions: Provide specific suggestions for improving the code, including code examples
    
    Output format:
    ## Code Function
    [Details]
    
    ## Code Quality Assessment
    [Detailed evaluation]
    
    Potential Problems
    - [Question 1]
    - [Question 2]
    ...
    
    ## Improvement Suggestions
    ``` {language}
    [Improved code example]
    ```
    [Explain the reason for improvement]
    """

    
    return ai_model.generate(prompt)

 

Best Practices for Prompt Word Optimization

 

1. Iterative Optimization

Cue engineering is an iterative process. First, a basic cue is created, and then it is continuously adjusted and optimized based on the AI's response. 

# Example of iterative optimization of prompt words
initial_prompt =  "Explain what Docker is"
response = ai_model.generate(initial_prompt)

# After evaluating the response, optimize the prompt
improved_prompt =  """
You are a DevOps expert. Please explain the concept of Docker using simple analogies so that people with no technical background can understand it.
The explanation should include:
1. Basic concepts of Docker
2. The main problem it solves
3. Differences from traditional virtualization technology
4. A real-life analogy

Limit to 300 words and use plain language.
"""

improved_response = ai_model.generate(improved_prompt)

2. Clarify context and constraints

Provide enough context and set constraints clearly. 

Background information: {Detailed background}
Target audience: {audience description}
Intended use: {Description of use}
Restrictions: {time/length/format, etc.}

Based on the above information, please {specific tasks}.

3. Creativity in using temperature control output

Most AI models allow you to set a “temperature” parameter that controls the randomness and creativity of the output. 

# Low temperature setting - more certain answers, suitable for factual tasks
factual_response = ai_model.generate(prompt, temperature= 0.2 )

# High temperature setting - more creative answers, suitable for creative tasks
creative_response = ai_model.generate(prompt, temperature= 0.8 )

Conclusion

In AI agent application development, prompt word design is like the art of communicating with advanced extraterrestrial intelligence. Mastering this art requires understanding the capabilities of AI, the ability to clearly express intentions, and the patience to experiment and iterate. 

Through the prompt word template techniques introduced in this article, developers can guide AI models more effectively and build smarter and more accurate applications. Remember, the best prompt words are usually clear, structured, provide sufficient context, and are optimized for specific tasks.