Prompt Engineering Best Practices for Developers

Introduction to Prompt Engineering

Prompt engineering is the art and science of crafting inputs that elicit the best possible outputs from large language models (LLMs). As AI models become more sophisticated, the ability to communicate effectively with them becomes increasingly valuable for developers.

Whether you're building AI-powered applications, automating workflows, or simply trying to get better results from ChatGPT, understanding prompt engineering principles will dramatically improve your outcomes.

Core Principles

1. Be Specific and Clear

Vague prompts lead to vague results. The more specific you are about what you want, the better the output will be.

2. Provide Context and Examples

Context helps the model understand your domain, constraints, and expectations. Examples demonstrate the format and style you're looking for.

I'm building a banking application that needs to validate credit card numbers. 
I need a TypeScript function that:
- Validates using the Luhn algorithm
- Supports Visa, Mastercard, and American Express
- Returns both validity and card type
- Handles edge cases (empty, non-numeric input)

Example usage:
validateCard("4532015112830366") // { valid: true, type: "visa" }
validateCard("invalid") // { valid: false, type: null }

3. Use Role-Based Prompting

Tell the AI what role to assume. This helps it understand the perspective and expertise level to apply.

Advanced Techniques

Chain of Thought Prompting

Ask the model to show its reasoning process step by step. This often leads to better results, especially for complex problems.

Analyze this React component for potential memory leaks. 
Think through this step by step:

1. First, identify all event listeners and subscriptions
2. Check if they're properly cleaned up in useEffect cleanup
3. Look for any closures that might capture stale references
4. Examine state updates that might happen after unmount
5. Provide specific recommendations for each issue found

[Component code here...]

Few-Shot Learning

Provide multiple examples to establish a pattern. This is particularly effective for formatting, style, or structure requirements.

Convert these function descriptions to TypeScript interfaces:

Example 1:
Description: "Function that takes a user ID and returns user profile"
Interface: interface GetUserProfile { (userId: string): Promise<UserProfile> }

Example 2:
Description: "Function that validates email and returns boolean"
Interface: interface ValidateEmail { (email: string): boolean }

Now convert:
Description: "Function that processes payment with amount and card details"

Constraint-Based Prompting

Set clear boundaries and constraints to guide the output format and scope.

Create a React component with these constraints:
- Maximum 50 lines of code
- Must use TypeScript
- Only use React hooks (no class components)
- Include proper error handling
- Add JSDoc comments for all props
- Use semantic HTML elements
- Ensure accessibility (ARIA labels, keyboard navigation)

Model-Specific Optimization

GPT-4 Strategies

• • Use system messages to set persistent context
• • Break complex tasks into smaller, sequential prompts
• • Leverage its strong reasoning capabilities with "think step by step"
• • Use temperature settings: lower (0.1-0.3) for code, higher (0.7-0.9) for creative tasks

Claude Strategies

• • Excellent at following detailed instructions and constraints
• • Strong with structured outputs (JSON, XML, tables)
• • Use clear hierarchical structure in prompts
• • Effective with role-playing scenarios

Common Pitfalls to Avoid

Real-World Use Cases

Code Review Automation

You are a senior developer conducting a code review. Analyze this pull request and provide feedback in the following format:

**Security Issues:** [List any security concerns]
**Performance Issues:** [List performance problems]
**Code Quality:** [Comment on readability, maintainability]
**Suggestions:** [Specific improvement recommendations]
**Approval Status:** [APPROVE/REQUEST_CHANGES/COMMENT]

Focus on:
- SQL injection vulnerabilities
- XSS protection
- Authentication/authorization
- Input validation
- Error handling

[PR diff here...]

API Documentation Generation

Generate OpenAPI 3.0 documentation for this Express.js route. Include:

Required fields:
- Path parameters with types and descriptions
- Request body schema with validation rules
- Response schemas for success (200) and error cases (400, 401, 500)
- Example requests and responses
- Security requirements

Format as valid YAML that can be imported into Swagger UI.

[Route code here...]

Testing and Iteration

Prompt engineering is an iterative process. Here's how to systematically improve your prompts:

1. 1. Baseline: Start with a simple, clear prompt
2. 2. Test: Run it multiple times to check consistency
3. 3. Analyze: Identify what's missing or incorrect in outputs
4. 4. Refine: Add constraints, examples, or context
5. 5. Validate: Test edge cases and different scenarios

Tools and Resources

Conclusion

Effective prompt engineering is a crucial skill for developers working with AI. By following these best practices—being specific, providing context, using appropriate techniques for your use case, and iterating based on results—you'll achieve significantly better outcomes from AI models.

Remember that prompt engineering is both an art and a science. While these principles provide a solid foundation, the best prompts often come from experimentation and understanding your specific domain and requirements.