LLM Prompt Engineering Best Practices

Introduction

Prompt engineering has become a crucial skill in the era of large language models (LLMs). This guide explores advanced techniques and best practices for crafting effective prompts that yield optimal results from AI models.

Understanding Prompt Components

1. Context Setting

The context provides the LLM with necessary background information:

You are an expert software developer with extensive experience in React and TypeScript.Your task is to review code and suggest improvements while maintaining best practices.

2. Task Specification

Clear and specific instructions help the model understand exactly what's required:

Review the following React component and suggest improvements for:1. Performance optimization2. Type safety3. Code organization4. Error handling

3. Format Definition

Specify the desired output format:

Please provide your response in the following format:- Issue identified- Explanation of the problem- Suggested solution with code example- Additional considerations

Advanced Techniques

Chain of Thought Prompting

Guide the model through a logical reasoning process:

Let's solve this step by step:1. First, analyze the current implementation2. Then, identify potential bottlenecks3. Finally, propose optimized solutions

Few-Shot Learning Examples

Provide examples to demonstrate the desired pattern:

Input: Function with potential memory leakAnalysis: The function doesn't cleanup event listenersSolution: Implement cleanup in useEffect hookInput: Untyped React componentAnalysis: Missing TypeScript interfacesSolution: Add proper type definitions

Common Patterns and Anti-patterns

Effective Patterns

✅ Be specific and explicit ✅ Use consistent formatting ✅ Include examples when relevant ✅ Break down complex tasks ✅ Request step-by-step explanations

Anti-patterns to Avoid

❌ Vague or ambiguous instructions ❌ Inconsistent formatting ❌ Overly complex requests ❌ Missing context ❌ Unclear success criteria

Case Studies

Case 1: Code Review Assistant

// Original PromptReview this code:function UserList() {  const [users, setUsers] = useState([]);  useEffect(() => {    fetch('/api/users').then(r => r.json()).then(setUsers);  }, []);  return <div>{users.map(u => <div>{u.name}</div>)}</div>;}// Improved PromptAnalyze this React component for:1. TypeScript type safety2. Error handling3. Loading states4. Performance optimization5. AccessibilityProvide specific improvements with code examples.

Case 2: API Documentation Generator

// Original PromptWrite API docs for this endpoint:POST /api/users// Improved PromptCreate comprehensive API documentation for this endpoint including:1. Endpoint specification (method, path, description)2. Request body schema with types3. Response format and status codes4. Authentication requirements5. Example requests and responses6. Error handling scenarios

Best Practices Checklist

1. Clarity and Precision

   • Use specific, unambiguous language
   • Define expected output format
   • Include success criteria

2. Structure and Organization

   • Break down complex tasks
   • Use consistent formatting
   • Include relevant examples

3. Context and Constraints

   • Provide necessary background information
   • Specify any limitations or requirements
   • Include relevant domain knowledge

4. Iteration and Refinement

   • Start with basic prompts
   • Iterate based on results
   • Maintain prompt versioning

Conclusion

Effective prompt engineering is both an art and a science. By following these best practices and continuously refining your approach, you can achieve more reliable and higher-quality results from LLMs.

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