AI for Everyone, Part 5: How to Talk to AI Effectively – 30 Golden Rules for AI Prompts

Ever found yourself wondering, "Why can't AI give me the answer I want?" or why some people can get accurate and useful answers from AI when asked the same question?

If you've ever felt frustrated by inaccurate AI responses, you're not alone. The key difference often lies in how you write AI prompts effectively — a core skill in AI prompt engineering.

For beginners, learning how to get better answers from AI starts with understanding AI prompt writing tips and avoiding common mistakes.

What Is AI Prompt Engineering? – Your Bridge to AI's Brain

An AI prompt, simply put, is the text command you type when you're talking to AI. It's more than just a quick question; it's like a key that unlocks AI's full potential. Every prompt you write acts as a communication bridge between you and the AI, determining if the AI grasps your intention, understands your needs, and delivers a useful response.

When you're using ChatGPT, Midjourney, or other AI tools in your daily life, it's common to run into situations where the "AI doesn't get it" or the "results aren't quite right." Often, this isn't an issue with the AI technology itself. Instead, it's usually due to a "communication gap" caused by unclear or vague prompts, or a lack of context.

Picture this: You walk into a foreign restaurant, and you don't speak the language. You might gesticulate wildly, trying to convey "I want something with chicken." The waiter might bring you fried chicken, chicken curry, or even chicken liver pâté—because your request wasn't specific enough, the outcome might be totally off-the-wall. An AI prompt, in this scenario, acts like a skilled interpreter, helping you clearly and accurately translate your needs into language and structure that AI can understand.

That's precisely why a well-crafted prompt can dramatically boost the quality of AI's responses, making the AI feel much more like a professional, dependable assistant. On the flip side, vague, incomplete, or context-less prompts will lead the AI to "guess" or "assume," often straying far from your actual intent.

Mastering AI prompt design skills (AI Prompt Engineering) does not require you to understand programming technology, but to learn to express our wishes more clearly. As long as you can accurately describe your needs, AI can perform tasks for you efficiently, whether it is writing articles, generating pictures, making PPTs, analyzing data, or even planning travel.

AI Prompt Techniques & Real-World Examples

1. Be Specific, Ditch the Vague Talk

Many people tend to ask AI overly broad questions, which predictably leads to equally broad and unhelpful answers.

Bad Example: "Write me some marketing copy."

Problem Analysis: This prompt is way too vague. The AI has no idea what kind of copy you need, who the audience is, or any specific requirements. The result will likely be generic and irrelevant.

Good Example: "Please write a social media marketing post for our organic skincare brand, targeting professional women aged 25-35. Emphasize the product's natural ingredients and anti-aging benefits. The tone should be light and friendly, and keep it under 100 words."

This prompt includes the target audience, product features, key selling points, tone, and word count, allowing the AI to generate content that truly fits your needs.

2. Role-Playing: Get AI to Act Like a Pro

By assigning AI a specific role, you can get it to think and respond from an expert's perspective.

Bad Example: "How do I lose weight?"

Problem Analysis: This question is too general. The AI can only offer generic advice, lacking specific guidance or professional depth.

Good Example: "Please act as a nutritionist with 10 years of experience. Create a one-month healthy weight loss plan for a 30-year-old woman who sits at an office desk all day, weighs 70kg (154 lbs), and is 165cm (5'5") tall. The plan should be scientifically sound and easy to follow."

Role-playing helps the AI approach the problem from an expert angle, providing far more valuable suggestions.

3. Let AI think in steps: Decomposing complex problems

For complicated questions, asking AI to think through them in specific steps often yields much better results.

Bad Example: "Analyze whether this business plan is any good."

Problem Analysis: Without a specific analytical framework, the AI struggles to provide a structured analysis.

Good Example: "Please analyze my coffee shop business plan by following these steps:

1. Market demand analysis;
2. Competitive landscape assessment;
3. Financial viability;
4. Risk assessment;
5. Improvement suggestions.

Explain the reasoning in detail for each step."

This prompt gives the AI a clear analytical framework, ensuring the response is comprehensive and logical.

4. Provide Examples: Guide AI with Specific Models

Sometimes, instead of just describing what you want, it's better to give the AI an example to emulate.

Bad Example: "Write an interesting opening."

Problem Analysis: "Interesting" is subjective; everyone has a different idea of what that means.

Good Example: "Please write an article opening in the following style: 'Have you ever had this experience: You're determined to go to bed early, but you end up scrolling on your phone late into the night? If so, congratulations, you've successfully joined the 'night owl' army.' Use a similar rhetorical question style to write a captivating opening for my time management article."

By providing a concrete style example, the AI can better understand your request and deliver precisely what you're looking for.

5. Set AI Output Format: Make Results More Practical

Clearly telling AI what format you want the answer in can make the results far more practical for your needs.

Bad Example: "Give me some fitness tips."

Problem Analysis: No format specified, so the AI might churn out a wall of text that's not practical for actual use.

Good Example: "Please create a one-week fitness plan for beginners. Requirements:

• Present it in a table format.
• Include specific exercises, sets, and reps for each day.
• Indicate suitable times and intensity levels.
• Provide simple instructions for each exercise."

This prompt tells the AI you need a structured, actionable fitness plan that's ready to use.

6. Context Continuity: Make AI Conversations More Coherent

In ongoing conversations, leveraging contextual information helps AI better understand your evolving needs.

Bad Example: (After discussing marketing strategies) "What about pricing?"

Problem Analysis: Sudden topic switch without clear context can confuse the AI.

Good Example: "Building on our discussion about marketing strategies for young adults, how do you think we should price our product? Please consider the target customers' purchasing power and the competitive market."

This prompt clearly signals that you're continuing the previous discussion, just drilling down into a specific aspect.

7. Setting Constraints: Make AI Responses More Accurate

By setting clear boundaries, you can guide AI to give you more accurate and useful answers.

Bad Example: "Recommend some good apps."

Problem Analysis: Without any restrictions, the AI might recommend a bunch of irrelevant apps.

Good Example: "Please recommend 5 apps for college students to boost study efficiency. Requirements:

• Must support iOS and Android.
• Must have a free version.
• Main functions should be time management and note organization.
• The interface should be clean and easy to use.
• For each app, explain its key features."

Setting specific constraints helps the AI provide highly relevant recommendations.

8. Scenario Simulation: Help AI Grasp the Use Case

Describing a specific scenario helps AI better understand the real-world context of your request.

Bad Example: "How do I talk to customers?"

Problem Analysis: Without a specific scenario, the AI can only offer generic communication advice.

Good Example: "I'm a new salesperson starting tomorrow. I need to pitch our enterprise software service to a client who's very price-sensitive. This client previously said our product was too expensive. Please help me prepare a communication script focusing on the product's value for money and long-term benefits."

A detailed scenario description enables the AI to provide highly targeted advice.

9. Negative Constraints: Tell AI What NOT to Do

Beyond telling AI what to do, explicitly telling it what not to do can be just as important.

Bad Example: "Write me a product description."

Problem Analysis: No restrictions, so the AI might write something overly exaggerated or inaccurate.

Good Example: "Please write a product description for our smartwatch. It must be factual and objective. Do not use exaggerated adjectives, do not mention unverified features, and do not compare it directly to competitors. Focus on the actual user experience and core functionalities."

Clear negative instructions help set boundaries and prevent the AI from generating inappropriate content.

10. Multiple Perspectives: Ask AI to Analyze from Different Angles

Having AI analyze a problem from several viewpoints leads to a more comprehensive breakdown.

Bad Example: "What do you think of this startup idea?"

Problem Analysis: Without specified angles, the AI might give a one-sided evaluation.

Good Example: "Please analyze my online education startup idea from these three perspectives:

1. Market Opportunity: Analyze the target market size and growth potential.
2. Competition: Assess existing competitors and our differentiation strategy.
3. Execution: Evaluate resource requirements and implementation difficulty."

Analyzing from multiple angles helps the AI provide a more well-rounded and objective response.

11. Time Constraints: Set Clear Time Frames for AI Responses

Giving tasks a time limit makes AI's suggestions more practical and actionable.

Bad Example: "Help me make a plan to learn AI."

Problem Analysis: No time limit means the AI can't suggest a concrete learning pace or milestones.

Good Example: "Please create a one-month plan for me to learn AI. I'm a beginner aiming to use AI for daily office tasks. I can dedicate 1 hour to study each day. Please outline specific learning content and practice exercises week by week."

A clear time frame helps AI create a more realistic and actionable plan.

12. Prioritization: Let AI Help You Choose

When facing multiple options, asking AI to prioritize can help you make better decisions.

Bad Example: "What skills should I learn?"

Problem Analysis: Without specific criteria, the AI struggles to offer valuable advice.

Good Example: "I'm a marketing specialist looking to boost my career competitiveness in the next year. Please recommend and rank the top 5 skills worth learning based on:

1. Market demand.
2. Learning difficulty.
3. Salary increase potential.
4. Relevance to my current job.

For each skill, explain your recommendation."

Clear ranking criteria enable the AI to provide more targeted advice.

13. Hypothetical Scenarios: Use "If" to Guide AI's Thinking

Setting up hypothetical situations can lead AI to deeper analysis and insights.

Bad Example: "My online store sales are bad."

Problem Analysis: Just stating a problem doesn't prompt AI to deep-dive into analysis.

Good Example: "Assume you're an e-commerce operations expert. My online store's sales dropped 30% last month. Please analyze the possible reasons and propose solutions. Assume the store was operating normally before and product quality isn't an issue. Analyze from three dimensions: traffic, conversion rate, and average order value."

Hypothetical scenarios encourage the AI to conduct more professional and in-depth analysis.

14. Comparative Analysis: Have AI Compare Options

Asking AI to perform comparative analysis can help you better understand the pros and cons of different choices.

Bad Example: "Which is better, Python or Java?"

Problem Analysis: Without specific comparison dimensions, the AI can only offer a superficial comparison.

Good Example: "Please compare Python and Java as programming languages across these dimensions:

1. Learning difficulty.
2. Job prospects.
3. Salary potential.
4. Application areas.
5. Community support.

I'm a programming beginner looking to choose a language for initial learning. Please give your recommendation."

Clear comparison dimensions help the AI provide a more objective and practical analysis.

15. Error Diagnosis: Let AI Help You Pinpoint Problems

When you hit a snag, asking AI to diagnose the problem can quickly point you towards a solution.

Bad Example: "My PowerPoint presentation isn't good."

Problem Analysis: Without a specific problem description, AI can't offer targeted advice.

Good Example: "I just finished a project report PPT, but it doesn't feel professional enough. Please help me diagnose potential issues and suggest improvements. The PPT is 20 pages, covering project background, execution process, results display, and future plans. It's for a presentation to senior company leadership, lasting 15 minutes."

Detailed background information helps the AI provide more accurate diagnoses and suggestions.

16. Sparking Creativity: Brainstorming Questions

When you need fresh ideas, ask AI to brainstorm with you.

Bad Example: "Give me some creative ideas."

Problem Analysis: Too broad; the AI won't know what kind of creative ideas you're looking for.

Good Example: "Our company is hosting its annual gala, and we need some fun interactive segments. Please brainstorm 10 creative ideas. Requirements:

1. Suitable for 100 participants.
2. Cost under $5,000.
3. Should help colleagues get to know each other better.
4. Doesn't require complex technical equipment.

For each idea, briefly describe its execution and expected effect."

Specific creative directions and constraints help the AI generate more practical ideas.

17. Template Generation: Have AI Create Reusable Frameworks

Asking AI to generate templates or frameworks can significantly boost your productivity.

Bad Example: "Help me write a work summary."

Problem Analysis: No template format specified, so the AI will just write a specific summary, which isn't reusable.

Good Example: "Please create a universal monthly work summary template for me. It should include these sections:

1. This month's work completion.
2. Key achievements showcased.
3. Problems and challenges encountered.
4. Next month's work plan.
5. Required resource support.

For each section, provide specific filling guidelines and examples."

Template-based requests lead to more practical and reusable content from AI.

18. Step-by-Step Refinement: Enabling AI to Continuously Improve Its Answers

Using a gradual refinement approach allows AI's output to get closer and closer to what you need.

Bad Example: "This plan isn't working, rewrite it."

Problem Analysis: No specific direction for improvement, so the AI doesn't know how to optimize.

Good Example: "Based on the marketing plan you just provided, please make the following adjustments:

1. Add online channel promotion strategies.
2. Make the budget allocation more detailed.
3. Include specific metrics for performance evaluation.
4. Consider seasonal factors.

Please retain the core ideas of the original plan."

Specific optimization directions allow AI to precisely refine the output.

19. Technical Jargon: Use Industry Language for Precision

When working in specialized fields, using accurate terminology helps AI provide more expert answers.

Bad Example: "How do I make my website rank better?"

Problem Analysis: Not enough specific jargon, so AI might give generic advice.

Good Example: "Please develop an SEO optimization strategy for me, focused on boosting organic search rankings. Include:

1. Keyword research and placement.
2. On-page content optimization.
3. Technical SEO improvements.
4. Backlink building strategies.
5. User experience optimization.

The goal is to get our main keywords into the top 3 search results within 3 months."

Using professional terminology helps AI provide more precise and expert recommendations.

20. Reverse Thinking: Guiding AI to Work Backward from the Result

By describing your desired outcome, you can have AI work backward to outline the process and methods to achieve it.

Bad Example: "I want to be successful."

Problem Analysis: "Success" is too vague for AI to give concrete guidance.

Good Example: "My goal is to become a department head at my company within two years; currently, I'm a regular employee. Please help me work backward to identify the necessary skills, experience, and opportunities needed to achieve this. Develop a detailed career development path, including specific action plans and timelines for each stage."

Clear goals and a reverse-thinking approach enable AI to map out a more precise path to achievement.

21. Emotional Intelligence & Tone Control: Conveying Feelings to AI

AI can handle more than just factual information; it can understand and respond to emotional nuances, but you need to convey the emotional context accurately.

Bad Example: "Write an apology letter."

Problem Analysis: Without emotional context, AI will produce a generic apology that lacks sincerity.

Good Example: "I missed my friend's birthday party because I was swamped with work, and she's really disappointed. Please help me write a sincere apology letter. The tone should be heartfelt but not overly self-blaming. I want to convey how much I value our friendship and propose a specific way to make it up to her. Avoid overly formal language."

Describing the specific emotional context and desired tone helps AI generate more impactful content.

22. Data Processing & Analysis: Make AI Your Data Assistant

When dealing with complex data, clear analytical instructions empower AI's robust analysis capabilities.

Bad Example: "Help me analyze this data."

Problem Analysis: Without a clear analytical goal or method, AI won't know what dimensions to analyze.

Good Example: "Please analyze the sales data I'm providing, which includes fields like product name, sales volume, price, region, and time. Perform the following analyses: 1) Calculate sales revenue and percentage by region; 2) Identify sales trends and seasonal patterns; 3) Pinpoint the best-selling and slowest-moving products; 4) Calculate the profit margin for each product; 5) Visualize key findings with charts."

Specific analysis dimensions and display requirements help AI perform in-depth data insights.

23. Multi-Turn Conversation Management: Keeping Long Dialogues Coherent

In complex, multi-turn conversations, effectively managing contextual information is crucial.

Bad Example: "Continue what we were talking about."

Problem Analysis: In a long conversation, AI might forget specific previous details, leading to disjointed responses.

Good Example: "Going back to our e-commerce operations strategy discussion, you mentioned three key points: user acquisition, conversion optimization, and customer retention. I'd like to deep-dive into conversion optimization now, specifically for our clothing brand. How can we improve the conversion rate from shopping cart to order?"

Explicitly referencing previous content and highlighting the current focus ensures conversation continuity.

24. Creativity and Inspiration Boost: Helping AI Break Through Mental Barriers

When you need innovative thinking, you can guide AI to step outside conventional frameworks.

Bad Example: "Give me some innovative ideas."

Problem Analysis: Too broad; AI might offer common, uninspired suggestions.

Good Example: "Using the following creative thinking methods, please generate ideas for transforming our traditional bookstore: 1) Reverse Thinking: If a bookstore didn't sell books, what else could it do? 2) Cross-Industry Fusion: What unexpected industries could a bookstore combine with? 3) User Experience Reimagination: How can visiting a bookstore become a totally new experience? 4) Tech Empowerment: What possibilities can new technologies create? For each direction, provide 3 specific ideas."

Specifying creative thinking frameworks helps AI generate more groundbreaking ideas.

25. Learning and Education Scenarios: Personalized Study Guidance

AI can be an excellent personalized learning assistant, but providing detailed learning background is key.

Bad Example: "Teach me programming."

Problem Analysis: Without specific learning goals or background, AI can only offer generic study advice.

Good Example: "I'm a marketing professional who wants to learn Python to automate data analysis. I have no prior programming experience and can dedicate 5 hours per week to studying. My goal is to be able to process Excel data, create charts, and generate simple data reports within 3 months. Please create a detailed study plan for me, including weekly content, practice projects, and evaluation criteria."

Detailed learning background and goals allow AI to create personalized learning plans.

26. Emergency & Troubleshooting: Correcting AI When It Misses the Mark

When AI's response veers off course, effective correction techniques can quickly get it back on track.

Bad Example: "No, try again."

Problem Analysis: Not pointing out the specific error means AI doesn't know how to improve.

Good Example: "Your previous response has a few issues that need correcting: 1) I was referring to B2B sales, not B2C. 2) Our target customers are small-to-medium businesses, not large enterprises. 3) The budget limit is $100,000, not $1,000,000. Please revise the marketing plan based on this accurate information."

Specifically pointing out errors and providing correct information allows AI to precisely adjust its response.

27. Ethical & Safety Considerations: Responsible AI Use

When using AI, it's crucial to be mindful of potential ethical and safety issues.

Bad Example: "Help me write an article attacking a competitor."

Problem Analysis: This request could involve unfair competition and defamation, making it inappropriate.

Good Example: "Please help me write an article objectively analyzing our product's advantages compared to competitors. Requirements: 1) Be based on facts, without exaggeration or defamation. 2) Focus on highlighting our unique value. 3) Maintain a professional and objective tone. 4) Avoid directly attacking competitors. 5) Provide valuable comparative information for users."

Expressing your needs in a positive and constructive way helps avoid generating harmful content.

28. Cross-Language and Cultural Adaptation: Guiding AI to Consider Cultural Differences

When content involves different cultural backgrounds, you need to guide AI to consider cultural nuances.

Bad Example: "Translate this marketing copy."

Problem Analysis: A simple translation might miss cultural nuances, making the content unsuitable for the target market.

Good Example: "Please adapt this skincare marketing copy, originally for the U.S. market, for a Japanese audience. Requirements: 1) Consider Japanese women's skincare habits and preferences. 2) Adjust the language style to fit the subtle characteristics of Japanese culture. 3) Modify color and image descriptions to align with local aesthetics. 4) Retain core selling points but adjust expression. 5) Ensure compliance with Japanese advertising regulations."

Considering cultural differences and localization needs makes the content more appropriate for the target market.

29. Collaboration & Team Application: Standardizing Team Usage

When using AI in a team environment, establishing standardized prompt guidelines is important.

Bad Example: "Everyone use AI to write reports."

Problem Analysis: Without unified standards, prompt quality will vary, leading to inconsistent output quality.

Good Example: "Please help us create a standardized AI usage guide for our team for generating project reports. It should include: 1) A unified report structure template. 2) Specific prompt examples for each section. 3) Quality control standards. 4) Solutions for common issues. 5) A training guide for team members. Ensure everyone can achieve consistent, high-quality output."

Establishing standardized processes ensures consistency and efficiency in team collaboration.

30. Metacognition & Reflection: Strategies for Continuous Improvement

Learning to evaluate and improve your prompt usage is an advanced skill for interacting with AI.

Bad Example: "This answer is bad."

Problem Analysis: Not thinking deeply about why the problem occurred makes continuous improvement difficult.

Good Example: "Please help me analyze the effectiveness of my previous prompt: My original need was X, my prompt was Y, and AI's answer was Z. Please evaluate: 1) Was my need expressed clearly? 2) Was the prompt structure logical? 3) What key information was missing? 4) How can I improve to get a better result? 5) What's the best prompting pattern for this type of question?"

By continuously learning and refining how we "talk" to AI, we can all become proficient users. In fact, chatting with AI is quite similar to getting to know someone new.

Common Mistakes in AI Prompting and How to Fix Them

Pitfall 1: Asking the AI too many questions at once

Many people cram multiple questions into a single prompt, which often leads to scattered or unfocused AI responses.

Solution: Focus on one core question at a time. If you have related follow-up questions, ask them in separate, sequential prompts.

Pitfall 2: Over-reliance on AI’s subjective judgment

Some questions require human subjective judgment. Over-relying on AI for these might lead to inappropriate advice.

Solution: Ask AI to provide objective information and various options, but reserve the final judgment for yourself.

Pitfall 3: Ignore repeated optimization AI prompts

Many users give up if they don't get a satisfactory answer on the first try. In reality, prompts can—and should—be refined.

Solution: Adjust your prompt based on AI's response, gradually tweaking it until you achieve the desired outcome.

Master AI Prompting: Communicate Effectively with AI

Mastering AI prompts is not an overnight achievement; it requires continuous practice and improvement in how you craft and use them. Every interaction with AI offers an opportunity to enhance your problem-solving skills.

The core principles of effective AI prompting are simple: clarify your goals, express your needs accurately, and patiently refine your requests.

In today’s digital world, learning AI prompting skills is as essential as mastering Google Search. With the right AI prompts, you can boost your problem-solving efficiency and make smarter decisions.

📚 Continue the Series:

AI for Everyone, Part 1: What is AI?

AI for Everyone, Part 2: How Does AI "Think" Like Humans?

AI for Everyone, Part 3: How AI is changing the world

AI for Everyone, Part 4: How to Learn AI as a Beginner: Step-by-Step Guide in 2025

AI for Everyone, Part 5: How to Talk to AI Effectively – 30 Golden Rules for AI Prompts