Prompt Engineering Best Practices Do’s and Don’ts

Do’s: Effective Prompt Engineering Techniques

1. Be Clear and Specific

• Why it’s effective: AI models respond better when given explicit instructions, reducing ambiguity.
• Example:
• ✅ Good Prompt: “Summarize this article in three bullet points, each under 20 words.”
• ❌ Bad Prompt: “Summarize this article.”
• Impact on AI responses: Ensures a structured, concise output.
• Real-world Scenario: When generating executive summaries, clarity ensures AI provides key takeaways instead of generic information.

2. Define the Output Format

• Why it’s effective: AI follows structured requests more accurately, ensuring usable responses.
• Example:
• ✅ Good Prompt: “Generate a JSON object with ‘title’, ‘author’, and ‘summary’ fields based on this article.”
• ❌ Bad Prompt: “Extract information from this article.”
• Impact on AI responses: Helps integrate AI responses into automated workflows without manual formatting.
• Real-world Scenario: When using AI for structured data extraction in database automation.

3. Use Step-by-Step Instructions

• Why it’s effective: Breaking down tasks makes AI more accurate in executing them.
• Example:
• ✅ Good Prompt: “1. Identify the main topic of the text. 2. List three supporting points. 3. Conclude with a one-sentence summary.”
• ❌ Bad Prompt: “Analyze this text.”
• Impact on AI responses: Produces detailed, logically structured responses.
• Real-world Scenario: Helpful for content analysis, academic research, or writing structured reports.

4. Provide Context and Constraints

• Why it’s effective: Helps AI understand the domain and desired tone.
• Example:
• ✅ Good Prompt: “Rewrite this email to sound professional and polite: [email text]. Keep it under 150 words.”
• ❌ Bad Prompt: “Make this email better.”
• Impact on AI responses: Produces tailored, context-aware responses.
• Real-world Scenario: Enhances communication effectiveness in business and marketing.

5. Specify the Perspective or Role

• Why it’s effective: AI adapts tone and style based on given roles.
• Example:
• ✅ Good Prompt: “As a cybersecurity expert, explain why strong passwords matter. Keep it concise.”
• ❌ Bad Prompt: “Explain why strong passwords are important.”
• Impact on AI responses: Ensures responses align with professional tone and expertise level.
• Real-world Scenario: Useful for role-specific advice, such as legal, medical, or technical recommendations.

Don’ts: Common Prompt Engineering Mistakes

1. Avoid Vague or Ambiguous Prompts

• Why it’s problematic: Leads to generic, unstructured responses.
• Example:
• ❌ Bad Prompt: “Tell me about AI.”
• ✅ Better Prompt: “Explain AI in simple terms for a 10-year-old, using an example.”
• Impact on AI responses: Lack of clarity results in broad, unfocused answers.
• Real-world Scenario: When teaching AI concepts to different audiences, specificity improves engagement and comprehension.

2. Don’t Overload with Too Many Requests

• Why it’s problematic: AI may prioritize some parts while neglecting others.
• Example:
• ❌ Bad Prompt: “Summarize this, analyze its tone, and rewrite it in a formal style.”
• ✅ Better Prompt: “Summarize this first, then analyze its tone in a separate response.”
• Impact on AI responses: Overcomplicating requests reduces accuracy.
• Real-world Scenario: When using AI for content refinement, breaking tasks into steps yields better results.

3. Don’t Assume Prior Knowledge Without Providing Context

• Why it’s problematic: AI relies on user input for context.
• Example:
• ❌ Bad Prompt: “Explain why this is important.”
• ✅ Better Prompt: “Explain why renewable energy adoption is crucial for reducing carbon emissions.”
• Impact on AI responses: Ensures AI provides relevant and accurate information.
• Real-world Scenario: Useful in technical writing and policy-making discussions.

4. Don’t Expect AI to Think Like a Human

• Why it’s problematic: AI lacks human intuition and must be guided explicitly.
• Example:
• ❌ Bad Prompt: “What do you think about this idea?”
• ✅ Better Prompt: “Analyze the feasibility of this idea using pros and cons.”
• Impact on AI responses: Encourages structured, logical responses rather than subjective speculation.
• Real-world Scenario: When brainstorming business strategies or technical solutions, clarity is key.

5. Avoid Overly Short or One-Word Prompts

• Why it’s problematic: Lacks details, leading to generic responses.
• Example:
• ❌ Bad Prompt: “Expand.”
• ✅ Better Prompt: “Expand on the key benefits of remote work with examples.”
• Impact on AI responses: Detailed prompts yield more insightful, informative answers.
• Real-world Scenario: Crucial when generating reports, articles, or research materials.

Conclusion

By following these best practices, you can harness AI’s capabilities effectively, ensuring high-quality responses tailored to your needs. Clarity, structure, and specificity lead to actionable and insightful AI-generated content, making AI a powerful tool for research, communication, and automation.