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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.

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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.
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.
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.
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.

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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.

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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 💡.

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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.

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Structured Output Requests

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

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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.
Master these foundational techniques to consistently get better results:
Don’t expect the perfect answer on your first try. Adjust your prompt each time based on the previous AI response.
1

Start with a basic prompt

Begin with a clear but simple question.
Explain how context windows work in AI models.
2

Evaluate the response

Check if the answer meets your needs. Was it too technical, too vague, or off-topic?
3

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.
4

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.
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.
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]
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

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Common Mistakes to Avoid

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.”
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.
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.”
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.

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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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Next Steps

Conversation Management

Learn how to manage long conversations and understand context windows.

Prompt Library

Save and reuse your best prompts with variables for efficiency.