Asking Great Questions

Getting high-quality responses from AI models starts with well-crafted questions. This guide progresses from basic principles to advanced techniques, helping you continuously improve your prompting skills.

The quality of AI responses directly correlates with the clarity and structure of your prompts. Small improvements in how you ask questions can dramatically improve results.

Basic Prompting Principles

The foundation of effective AI interaction:

Be clear and specific - Vague questions produce vague answers.
Use positive instructions - Focus on what you want, not what you don’t want.
Provide context - Give relevant background information to tailor responses.

Be clear and specific

The more specific your request, the better the response.Instead of:

Tell me about marketing.

Try:

Explain the three most effective digital marketing strategies for 
a B2B SaaS company with a $10,000 monthly budget.

Use positive instructions

Focus on what you want the AI to do, rather than what you don’t want.Instead of:

Don't be too technical.

Try:

Explain this in simple terms that a non-technical business owner 
would understand.

Provide context

Give the AI relevant background information to tailor its response.Example:

I'm a product manager at a fintech startup. We're deciding between 
microservices and monolithic architecture for our MVP. What factors 
should we consider given our 3-month launch timeline and team of 
4 developers?

Think of the AI as a knowledgeable colleague who needs context to give you the best advice.

Context Setting Techniques

Proper context ensures the AI understands your goals, audience, and constraints.

Define Your Audience

Specify who will use or read the response to adjust complexity and tone.

Set the Scope

Clearly define boundaries to prevent overly broad or narrow responses.

Provide Examples

Show examples of what you’re looking for to guide the output style.

State Your Goal

Explain what you’ll do with the response to get more actionable results.

Effective Context-Setting Pattern

Use this structure to provide comprehensive context:

[Role/Perspective]: I'm a [your role] working on [project/task]

[Situation]: Currently, [describe your situation]

🎯 [GOAL]: What I need to achieve

📏 [SCOPE - Constraints]: 
- [Constraint 1]
- [Constraint 2]
- [Constraint 3]

👥 [AUDIENCE]: Who will use/read this or explain the format the output needs to have

💡 [EXAMPLES]: Relevant examples or current approach (if applicable)

[OUTPUT]: Specify the exact format you want for the answer here. For example, request a table, summary, list of action steps, email draft, or bullet points - whatever would be most useful for your people or stakeholders.

[Your specific question]

Example:

Role: I'm a data analyst at a healthcare startup

Situation: We have patient satisfaction survey data from 500 respondents 
across 3 clinic locations over 6 months.

🎯 GOAL: Identify the top 3 factors affecting patient satisfaction to 
present to our executive team

📏 SCOPE - Constraints:
- Focus on actionable insights our clinics can implement
- Results must be statistically significant (p < 0.05)
- 6-month timeframe across 3 clinic locations
- Use Tableau for data visualization (required by our organization)

👥 AUDIENCE: Non-technical executives who need clear, actionable insights
- Prefer visual presentations over raw statistics
- Need to understand business impact

💡 EXAMPLES: Current approach includes basic satisfaction scores, but 
executives have requested deeper analysis linking satisfaction to specific 
operational factors

Output:
Please provide your answer in the following format:

- [Factor Name]: [Brief explanation or insight]
- [Factor Name]: [Brief explanation or insight]
- [Factor Name]: [Brief explanation or insight]

For example:
- Waiting times: How quickly patients are attended impacts satisfaction
- Staff friendliness: Positive staff interactions improve experience
- Clarity of instructions: Patients value clear after-visit information

Question: What analysis approach would you recommend, and what 
visualization types should I use for the executive presentation?

This structure applies all four context-setting techniques: stating your goal 🎯, defining your audience 👥, setting the scope 📏, and providing relevant examples 💡.

See It In Action: Video Example

Watch the following video for a practical demonstration of using these best practices to instruct an AI agent and achieve high-quality results.

This video guides you step-by-step in applying clarity, positive instructions, and effective context setting when prompting an AI agent.

Structured Output Requests

Requesting specific formats helps you get responses that are immediately usable.

Lists and Bullet Points

Request organized information in scannable format.Example:

List the 5 most important considerations when choosing a database 
for a real-time analytics platform. For each point:
- State the consideration
- Explain why it matters
- Provide one concrete example

Tables and Comparisons

Get side-by-side comparisons for decision-making.Example:

Create a comparison table of React, Vue, and Angular with these columns:
- Framework name
- Learning curve (easy/moderate/steep)
- Best use case
- Community size
- Enterprise adoption

Step-by-Step Instructions

Request procedures in sequential format.Example:

Provide step-by-step instructions to set up CI/CD for a Node.js 
application using GitHub Actions. Include:
1. Prerequisites needed
2. Each configuration step
3. How to verify each step worked
4. Common troubleshooting tips

Code Examples

Request working code with explanations.Example:

Write a Python function to validate email addresses using regex.
Include:
- Complete, runnable code with type hints
- Inline comments explaining the regex pattern
- Example usage
- Edge cases it handles

Output Length and Format

Specify desired length and structure to get appropriately detailed responses.Examples:

Summarize this in exactly 3 bullet points.
Provide a comprehensive analysis (approximately 500 words).
Give me a brief one-sentence answer first, then expand with details.

Prompt Engineering

Use tailored prompt engineering strategies to get high-quality, targeted responses from AI. Choose the approach that matches your needs and your current experience level.

Basic Strategies
Advanced Techniques

Master these foundational techniques to consistently get better results:

Iterative refinement

Don’t expect the perfect answer on your first try. Adjust your prompt each time based on the previous AI response.

Start with a basic prompt

Begin with a clear but simple question.

Explain how context windows work in AI models.

Evaluate the response

Check if the answer meets your needs. Was it too technical, too vague, or off-topic?

Refine and add specificity

Adjust your question to clarify what you want.

Explain how context windows work in AI models in simple terms. 
Use an analogy to help a non-technical person understand, and 
explain why this matters for everyday AI users.

Iterate until satisfied

Repeat this process until the answer matches your needs.

You’ll learn what works best for your goals by refining each time.

Building on responses

Use the AI’s previous answers as a springboard for further exploration or greater depth.Pattern:

First prompt: "Explain the benefits of microservices architecture."

Follow-up: "You mentioned scalability as a key benefit. Can you provide 
a concrete example of how microservices enabled scalability for a company 
handling rapid growth?"

Further follow-up: "Given those scalability benefits, what are the main 
challenges small teams face when adopting microservices?"

This approach leads to more natural, conversational, and productive exploration of complex topics.

Assign roles for perspective

Ask the AI to take on a specific professional or subject matter expert role.Example:

You are an experienced DevOps engineer with expertise in Kubernetes. 
Review this deployment configuration and identify potential security 
vulnerabilities and performance bottlenecks.

[Your configuration here]

Use constraints effectively

Clearly specify response rules to limit scope, structure, or style.Example:

Analyze this dataset with the following constraints:
- Focus only on Q4 2024 data
- Exclude outliers beyond 2 standard deviations
- Present findings in order of business impact
- Include statistical confidence levels

Level up your prompting with these more sophisticated approaches:

Chain-of-thought prompting

Ask for step-by-step reasoning to see how the AI arrives at a solution.Example:

Solve this problem step-by-step, explaining your reasoning at each stage:

Our e-commerce site has a 68% cart abandonment rate. Average cart 
value is $120. We have 50,000 monthly visitors with a 12% add-to-cart 
rate. Calculate the potential monthly revenue increase if we reduce 
abandonment to 55%, and explain what interventions might achieve this.

Show your calculations and reasoning for each step.

Why it works: Step-by-step requests generate more transparent, verifiable answers and reduce mistakes.

Few-shot learning

Show a few example inputs and the type of answers you expect. The AI will mimic your structure and approach.Example:

I'll show you examples of customer feedback and how I want them 
categorized, then you categorize new feedback the same way:

Example 1:
Feedback: "The app crashes every time I try to upload photos"
Category: Bug - Critical
Sentiment: Frustrated
Action: Escalate to engineering

Example 2:
Feedback: "Love the new dark mode! Makes it easier to use at night"
Category: Feature feedback - Positive
Sentiment: Satisfied
Action: Share with product team

Example 3:
Feedback: "It would be great if we could export data to CSV"
Category: Feature request
Sentiment: Neutral
Action: Add to feature backlog

Now categorize this feedback:
"The search function doesn't find exact matches sometimes"

Zero-shot prompting

Use this when your task is simple and expectations are obvious - no examples needed.Best for:

Clear tasks like summarization, translation, or classification
When you want unbiased answers

Example:

Translate the following English text to French, maintaining the 
professional tone:

[Your text here]

Prompt chaining

Break bigger challenges into smaller, sequential prompts. Use the output from one step as the input to the next.Example sequence:

Prompt 1: "Extract all customer pain points mentioned in these 
support tickets: [tickets]"

Prompt 2: "Take these pain points: [output from prompt 1] 
and group them into themes."

Prompt 3: "For each theme: [output from prompt 2], suggest 
one product improvement that addresses multiple pain points."

Why chain prompts:

Breaks up complex tasks
Lets you check (and fix) partial steps
Increases accuracy on multi-stage workflows

Template creation

Save time by building your own prompt templates for tasks you repeat often. Store them in the Prompt Library.Template Example:

Analyze this {{document_type}} for a {{target_audience}} audience.

Document:
{{content}}

Provide:
1. Executive summary (2-3 sentences)
2. Key insights (3-5 bullet points)
3. Recommended actions
4. Potential risks or considerations

Format: {{output_format:bullets|paragraphs|table}}

For details on managing and sharing prompts, see the Prompt Library.

Using tools in prompts

Direct the AI to use plugins or integrations when available, like business data connectors.Example:

Use the database connection tool to query our customer table and find 
all customers who signed up in the last 30 days but haven't made a 
purchase. Then analyze the data and suggest targeted retention strategies.

For more on AI tool integration, visit Tools Connection.

For further learning on prompt engineering, see guides from OpenAI, Anthropic, and Google.

Common Mistakes to Avoid

Being Too Vague

Problem: “Tell me about AI.”Better: “Explain the key differences between supervised and unsupervised machine learning for a business analyst evaluating which approach to use for customer segmentation.”

Asking Multiple Questions at Once

Problem: “How do I improve our SEO, should we use social media ads, and what content strategy works best for B2B?”Better: Break into separate, focused prompts or explicitly request separate treatment of each question.

Providing No Context

Problem: “Is this a good approach?” [without explaining what ‘this’ is or what your goals are]Better: Provide the approach, your context, your goals, and criteria for “good.”

Assuming Prior Knowledge

Problem: Starting a new conversation with “Continue from where we left off” without re-establishing context.Why this fails: AI models cannot access information from previous conversations. Each new conversation starts with an empty context window, so the AI has no way to retrieve or reference what was discussed before.

Learn more about context windows and how they work in Conversation Management.

Better: Briefly summarize relevant context from previous conversations when starting a new chat.

Best Practices Summary

Start Simple

Begin with a clear basic prompt, then add complexity as needed.

Be Specific

Vague questions get vague answers. Provide details and context.

Iterate

Refine your prompts based on the responses you receive.

Save What Works

Build a library of effective prompts for recurring tasks.

The best prompt is one that gets you the result you need. Experiment with different approaches and learn what works for your use cases.

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Basic Prompting Principles

Context Setting Techniques

Define Your Audience

Set the Scope

Provide Examples

State Your Goal

Effective Context-Setting Pattern

See It In Action: Video Example

Structured Output Requests

Prompt Engineering

Common Mistakes to Avoid

Best Practices Summary

Start Simple

Be Specific

Iterate

Save What Works

Next Steps

Conversation Management

Prompt Library

​Basic Prompting Principles

​Context Setting Techniques

Define Your Audience

Set the Scope

Provide Examples

State Your Goal

​Effective Context-Setting Pattern

​See It In Action: Video Example

​Structured Output Requests

​Prompt Engineering

​Common Mistakes to Avoid

​Best Practices Summary

Start Simple

Be Specific

Iterate

Save What Works

​Next Steps

Conversation Management

Prompt Library

Basic Prompting Principles

Context Setting Techniques

Effective Context-Setting Pattern

See It In Action: Video Example

Structured Output Requests

Prompt Engineering

Common Mistakes to Avoid

Best Practices Summary

Next Steps