Prompt Engineering

Prompt Engineering is the art and science of creating effective requests to artificial intelligence models to achieve desired results. In the context of Vibe Coding, where AI is used to generate code, the quality and structure of prompts are crucial for project success. This article provides a detailed guide to creating effective prompts for Vibe Coding that will help you obtain high-quality, reliable, and maintainable code.

Fundamentals of Prompt Engineering for Vibe Coding

Understanding the Context Window

The context window is the amount of information an AI model can consider when generating a response. Modern models have limited context windows (e.g., 8K-100K tokens), which affects how you should structure your requests.

For effective use of the context window:

Start with the most important information
Break complex tasks into smaller parts
Use references to previous parts of the conversation instead of full repetition
Remove redundant information that doesn’t add value

Structure of an Effective Prompt

A well-structured prompt for Vibe Coding typically contains the following elements:

Project Context: Brief description of the project, its goals, and constraints
AI Role: Specification of what role the AI should play (e.g., “Act as an experienced Python developer”)
Specific Task: Clear description of what needs to be done
Technical Requirements: Specifications, constraints, coding standards
Output Format: How the result should be presented
Examples: Samples of desired output (if necessary)

Example of a structured prompt:

Context: I'm developing a task management web application using React and Node.js.

Role: Act as an experienced fullstack developer with deep knowledge of React, Express, and MongoDB.

Task: Create a React component for displaying a list of tasks with the ability to mark completed tasks.

Technical Requirements:
- Use functional components and hooks
- Follow clean code principles
- Ensure error handling
- Add comments to complex parts

Output Format: Provide the complete component code with imports and exports.

Strategies for Different Programming Tasks

Generating New Code

When requesting new code creation, it’s important to provide sufficient context and clear requirements.

Effective strategies:

Start with a high-level description of functionality
Specify desired design patterns
Define interfaces and contracts
Indicate preferred libraries and frameworks
Ask the AI to first provide a plan or pseudocode

Example prompt:

Create a REST API in Express.js for user management with the following endpoints:
- GET /users - retrieve a list of users
- GET /users/:id - get information about a specific user
- POST /users - create a new user
- PUT /users/:id - update user information
- DELETE /users/:id - delete a user

Requirements:
- Use MVC architecture
- Include input validation
- Ensure error handling
- Use asynchronous functions with async/await
- Add comments in JSDoc format

First provide the project structure and describe the architecture, then implement each file separately.

Debugging and Error Fixing

For effective debugging with AI, it’s important to provide the error context and relevant code.

Effective strategies:

Include the complete error message
Provide the call stack
Indicate what you’ve already tried
Include relevant parts of the code
Describe the expected behavior

Example prompt:

I'm getting the following error when running my React application:

"TypeError: Cannot read property 'map' of undefined"

Here's the component causing the error:

```jsx
function TaskList({ tasks }) {
  return (
    <div>
      <h2>Tasks</h2>
      <ul>
        {tasks.map(task => (
          <li key={task.id}>{task.title}</li>
        ))}
      </ul>
    </div>
  );
}

The component is used like this:

<TaskList />

I expect the component to display a list of tasks or an empty list if there are no tasks. Help me find and fix the error.


### Refactoring and Optimization

When requesting code refactoring or optimization, it's important to specify concrete goals and constraints.

Effective strategies:
- Clearly define refactoring goals (readability, performance, maintainability)
- Indicate which parts of the code should remain unchanged
- Provide metrics or success criteria
- Ask the AI to explain its decisions

Example prompt:

Refactor the following function to improve performance and readability:

function processData(data) {
  let results = [];
  for (let i = 0; i < data.length; i++) {
    if (data[i].active === true) {
      let item = {
        id: data[i].id,
        name: data[i].name,
        score: data[i].points * 2
      };
      results.push(item);
    }
  }
  return results;
}

Refactoring goals:

Use modern JavaScript methods (map, filter, etc.)
Improve performance for large datasets
Make the code more readable and maintainable

After refactoring, explain what changes were made and why they improve the code.


### Code Documentation

AI can be very useful for creating documentation for existing code.

Effective strategies:
- Specify the desired documentation format (JSDoc, DocStrings, README, etc.)
- Define the level of detail
- Ask to include usage examples
- Specify the target audience for the documentation

Example prompt:

Create JSDoc documentation for the following class:

class DataProcessor {
  constructor(config) {
    this.config = config;
    this.cache = new Map();
  }

  async fetchData(endpoint) {
    if (this.cache.has(endpoint)) {
      return this.cache.get(endpoint);
    }
    
    const response = await fetch(`${this.config.apiUrl}/${endpoint}`);
    if (!response.ok) {
      throw new Error(`API error: ${response.status}`);
    }
    
    const data = await response.json();
    this.cache.set(endpoint, data);
    return data;
  }

  processItems(items, transformer) {
    return items.map(transformer).filter(item => item !== null);
  }

  clearCache() {
    this.cache.clear();
  }
}

The documentation should include:

Description of the class and its purpose
Constructor parameters
Description of each method with parameters and return values
Examples of class usage
Possible exceptions


## Advanced Prompt Engineering Techniques

### Chain of Thought

The Chain of Thought technique suggests that the AI reason step-by-step about a problem before providing the final solution. This is especially useful for complex programming tasks.

Example prompt:

Task: Implement Dijkstra’s algorithm for finding the shortest path in a graph in Python.

Please reason as follows:

First, determine what data structures will be needed
Then describe the main steps of the algorithm
Consider edge cases and optimizations
Finally, write the complete implementation with comments

Let’s solve this problem step by step.


### Role Prompts

Assigning the AI a specific role can significantly improve the quality of generated code.

Example prompt:

Role: You are an experienced Python developer with 15 years of experience, specializing in performance optimization and clean code. You follow PEP 8 principles and always write tests.

Task: Implement a class for caching heavy computation results with the ability to set TTL for cached values.

Provide a complete implementation of the class, including documentation and usage examples. Also add unit tests using pytest.


### Iterative Refinement

Instead of trying to get the perfect result on the first try, use an iterative approach with gradual refinement.

Process:
1. Start with a basic request
2. Evaluate the result
3. Refine the request based on the result received
4. Repeat until you get the desired result

Example iteration:

Iteration 1: “Create a simple REST API in Express.js for task management”

Iteration 2: “Thank you. Now add input validation using Joi and error handling”

Iteration 3: “Great. Now modify the API to support pagination and filtering when retrieving the task list”


### Using Examples (Few-Shot Prompting)

Providing examples of desired output can significantly improve the quality of generated code.

Example prompt:

Task: Create utility functions for working with arrays in JavaScript.

Here’s an example of the format and style I expect:

/**
 * Groups array elements by the specified key
 * @param {Array} array - Source array
 * @param {string|Function} key - Key or function for grouping
 * @returns {Object} Grouped elements
 */
function groupBy(array, key) {
  return array.reduce((result, item) => {
    const groupKey = typeof key === 'function' ? key(item) : item[key];
    result[groupKey] = result[groupKey] || [];
    result[groupKey].push(item);
    return result;
  }, {});
}

Now create the following functions in the same style:

chunk(array, size) - splits an array into groups of specified size
unique(array) - returns an array with unique elements
flatten(array) - converts a multidimensional array into a one-dimensional array


## Optimizing Prompts for Different AI Models

### Working with GPT-4

GPT-4 has an extended context window and improved reasoning capabilities, making it a powerful tool for Vibe Coding.

Recommendations:
- Use the Chain of Thought technique for complex tasks
- Don't be afraid to give detailed instructions
- Use multi-stage requests for complex projects
- Request alternative approaches to solutions

### Working with Claude

Claude from Anthropic is particularly good at handling long contexts and following instructions.

Recommendations:
- Use structured instructions with clear sections
- Explicitly state limitations and requirements
- Use XML markers or other delimiters to structure the request
- Request explanations of generated code

### Working with Gemini

Gemini from Google has strengths in understanding code and technical documentation.

Recommendations:
- Provide context in the form of documentation or specifications
- Use technical terms and precise descriptions
- Request code with comments and documentation
- Use an iterative approach for complex tasks

## Common Mistakes and How to Avoid Them

### Too Vague Requests

Vague requests lead to unpredictable results. For example, the request "Write code for a web application" is too general and doesn't give the AI enough information to generate useful code.

How to fix:
- Specify concrete functionality
- Define the technical stack
- Indicate constraints and requirements
- Provide examples of desired results

### Ignoring Project Context

Requests without project context can lead to code that doesn't integrate with the existing system.

How to fix:
- Briefly describe the project architecture
- Indicate libraries and frameworks used
- Provide information about existing components
- Specify project coding standards

### Lack of Verification and Validation

Blindly accepting generated code without verification can lead to the introduction of errors and vulnerabilities.

How to fix:
- Always check generated code
- Request explanations for complex parts
- Test code before integration
- Use static code analysis

### Information Overload

Providing too much information can overwhelm the model and lead to loss of focus.

How to fix:
- Focus on relevant information
- Break complex tasks into subtasks
- Use an iterative approach
- Structure information with clear sections

## Measuring and Improving Prompt Effectiveness

### Effectiveness Metrics

The following metrics can be used to evaluate prompt effectiveness:
- Accuracy of generated code (compliance with requirements)
- Number of iterations to achieve desired result
- Time spent formulating the request and processing the result
- Number of errors in generated code
- Maintainability and readability of generated code

### Maintaining a Prompt Library

Creating and maintaining a library of effective prompts can significantly increase productivity.

Recommendations:
- Document successful prompts with result examples
- Categorize prompts by task types
- Note which AI models the prompt works best with
- Regularly update the library based on new experiences

### A/B Testing Prompts

A/B testing can be used to determine the most effective formulations.

Process:
1. Create several prompt variants for one task
2. Test each variant with the same parameters
3. Evaluate results according to chosen metrics
4. Select the most effective variant and iteratively improve it

## Conclusion

Prompt engineering is a key skill for effective use of Vibe Coding. Well-structured, specific, and contextual prompts can significantly improve the quality of generated code and reduce development time.

Remember that prompt engineering is an iterative process requiring practice and experimentation. With experience, you'll learn to formulate requests that consistently lead to high-quality results.

Using the strategies and techniques described in this article, you'll be able to maximize the capabilities of AI to create high-quality, reliable, and maintainable code.

Last updated on March 25, 2025