Prompt Engineering Guides

Basic Prompting Techniques

Zero-Shot Prompting

The simplest form: just ask the model to perform a task with no prior examples.

Classify the following movie review as POSITIVE, NEUTRAL, or NEGATIVE.

Review: "The movie was an interesting attempt, but the plot felt underdeveloped."
Sentiment:

One-Shot Prompting

Provide a single example to demonstrate the desired format and style.

Extract the company name from the email signature.

Example:
Email: "Best regards, John Smith, Senior Developer at TechCorp"
Company: TechCorp

Email: "Sincerely, Sarah Johnson, Marketing Lead at InnovateLabs"
Company:

Few-Shot Prompting

Provide multiple examples (2-5) to teach the model the desired pattern and format.

Translate English to French.

sea otter => loutre de mer
cheese => fromage
beautiful => belle
mountain => montagne
- - -
car =>

System Prompting

Set the AI's role and behavior with system-level instructions.

SYSTEM: You are a helpful customer service representative for an online bookstore. Always be polite, empathetic, and offer solutions.

USER: I ordered a book last week but it hasn't arrived yet. I'm getting frustrated.

Role Prompting

Ask the AI to assume a specific role or persona for specialized responses.

Act as a senior software architect. Review this code and suggest improvements:

[CODE BLOCK]

Focus on scalability, maintainability, and performance considerations.

Contextual Prompting

Provide relevant background context to help the AI understand the situation better.

Context: You're helping a small startup (5 employees) choose between cloud platforms for their first web application. They have limited budget and technical expertise.

Question: Should we use AWS, Google Cloud, or Azure for hosting our React app with a Node.js backend?

Advanced Prompting Techniques

Chain-of-Thought (CoT)

Encourage the model to "think step-by-step" to solve complex reasoning problems.

Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?

A: Roger started with 5 balls. 2 cans of 3 tennis balls is 6 balls. 5 + 6 = 11. The answer is 11.

Tree of Thoughts (ToT)

Explore multiple reasoning paths simultaneously for complex problem-solving.

Problem: Plan a 3-day trip to Tokyo

Let me explore three different approaches:

Path 1 (Culture): Day 1: Temples, Day 2: Museums, Day 3: Traditional districts
Path 2 (Modern): Day 1: Shibuya/Harajuku, Day 2: Tech districts, Day 3: Skyscrapers
Path 3 (Mixed): Day 1: Traditional morning + modern evening, Day 2: Nature + city, Day 3: Food tour

Evaluating each path for feasibility, cost, and experience quality...

ReAct (Reason + Act)

Combine reasoning with actions to solve problems that require information gathering.

Question: What's the weather like in the capital of Japan?

Thought: I need to find the capital of Japan first, then get weather information.
Action: The capital of Japan is Tokyo.
Thought: Now I need current weather data for Tokyo.
Action: [Search for Tokyo weather]
Observation: Current temperature is 22°C, partly cloudy
Thought: I have the information needed to answer.
Answer: Tokyo (Japan's capital) currently has partly cloudy weather at 22°C.

Self-Consistency

Generate multiple reasoning paths and select the most consistent answer.

Problem: If a store has a 20% off sale, and then applies an additional 10% discount, what's the total discount?

Path 1: 20% + 10% = 30% total discount
Path 2: First 20% off $100 = $80, then 10% off $80 = $72, so 28% total discount
Path 3: Compound discount = 1 - (0.8 × 0.9) = 1 - 0.72 = 28% total discount

Paths 2 and 3 agree: 28% total discount is correct.

Step-Back Prompting

Ask broader questions to establish context before tackling specific problems.

Specific Question: How do I optimize this React component's performance?

Step-Back Question: What are the general principles of React performance optimization?

General Answer: React performance is optimized through:
- Minimizing re-renders (React.memo, useMemo, useCallback)
- Code splitting and lazy loading
- Virtual scrolling for large lists
- Optimizing bundle size

Now applying to your specific component: [specific optimization strategy]

Prompt Recipes: Ready-to-Use Templates

Copy-and-paste templates for common tasks. Just fill in the blanks!

The Quick Summarizer

Summarize the following text in [number] key bullet points. Identify the main argument, the evidence used, and the conclusion.

[Paste text here]

The ELI5 (Explain Like I'm 5)

Explain the concept of [complex topic, e.g., "black holes"] to me as if I were 5 years old. Use a simple analogy.

The Creative Brief

You are a [role, e.g., "marketing expert"]. Create a [deliverable] for [target audience] about [topic].

Requirements:
- Tone: [formal/casual/persuasive]
- Length: [word count]
- Include: [specific elements]

The Problem Solver

I'm facing this challenge: [describe problem]

Please provide:
1. Three possible solutions
2. Pros and cons of each
3. Your recommended approach with reasoning

The Code Reviewer

Review this [language] code for:
- Performance issues
- Security vulnerabilities
- Best practice violations
- Code readability

[paste code here]

Provide specific suggestions for improvement.

🚀 POML: Next-Generation Prompt Engineering

Master Microsoft's revolutionary Prompt Orchestration Markup Language - the future of structured, maintainable AI prompts.

New TechnologyIndustry Standard40% More Efficient

POML Fundamentals: The Future of Prompt Engineering

What is POML?

POML (Prompt Orchestration Markup Language) is Microsoft's revolutionary approach to structured prompt engineering. It brings the power of markup languages like HTML to AI prompting, making prompts more organized, maintainable, and powerful.

Key Benefits:

• 40% higher efficiency in crafting complex prompts
• 65% reduction in version control conflicts
• 30% boost in team productivity
• Improved maintainability and reusability

❌ Traditional Prompting

You are a helpful teacher. Explain photosynthesis to a 10-year-old using simple language. Keep it under 100 words and make it engaging. Use the attached diagram if helpful.

Problems:

Hard to parse and modify
No structure or organization
Difficult to reuse components
Version control challenges

✅ POML Structured

<poml>
  <role>You are a patient teacher explaining concepts to a 10-year-old.</role>
  <task>Explain photosynthesis using the provided image.</task>
  <img src="photosynthesis_diagram.png" alt="Diagram of photosynthesis"/>
  <output-format>
    Start with "Hey there, future scientist!" and keep under 100 words.
  </output-format>
</poml>

Benefits:

Clear semantic structure
Easy to modify components
Reusable and modular
Version control friendly

Core POML Concepts

Semantic Tags

Structure your prompts with meaningful components:

<role> - Define AI's personality

<task> - Specify what to do

<example> - Provide demonstrations

<output-format> - Set constraints

Data Integration

Embed various data types seamlessly:

<img> - Images

<document> - Text files

<table> - Structured data

<csv> - CSV data

Styling System

Control presentation with CSS-like styling:

style="verbose"

style="bullet"

format="json"

length="short"

Your First POML Prompt

Here's a complete example showing how to structure a content creation prompt:

<poml>
  <role>You are a creative content writer specializing in engaging blog posts.</role>
  
  <task>
    Write a compelling blog post introduction about sustainable living tips
    that will hook readers and encourage them to continue reading.
  </task>
  
  <example>
    <input>Topic: Zero waste lifestyle</input>
    <output>
      Did you know that the average person generates 4.5 pounds of waste every single day? 
      That's over 1,600 pounds per year! But what if I told you that you could cut that 
      number by 90% without sacrificing comfort or convenience?
    </output>
  </example>
  
  <output-format length="short">
    - Start with a surprising statistic or question
    - Keep under 150 words
    - End with a hook that promises value
    - Use conversational tone
  </output-format>
</poml>

Why This Structure Works:

🎭 Clear Role: Establishes the AI's expertise and writing style
🎯 Focused Task: Specific, actionable instruction
📝 Concrete Example: Shows exactly what good output looks like
📏 Output Constraints: Sets boundaries and expectations

Ready to Get Started?

POML transforms how you think about prompt engineering. Instead of crafting monolithic text blocks, you build modular, maintainable prompt architectures.

Next Steps:

1. 📖 Learn POML syntax and components
2. 🛠️ Try our POML-enabled Prompt Builder
3. 📋 Browse POML templates in our library
4. 🏆 Earn your POML certification

POML Syntax & Components: Building Structured Prompts

Core Semantic Tags

POML provides a rich set of semantic tags to structure your prompts logically and maintainably.

Essential Structure

<poml> - Root element

<role> - AI personality/expertise

<task> - Primary instruction

<instructions> - Detailed guidance

Enhancement Tags

<example> - Show desired output

<output-format> - Format constraints

<context> - Background information

<constraints> - Limitations/rules

The <role> Tag: Setting the Foundation

Basic Role Definition

<role>You are an experienced data scientist</role>

Simple, straightforward role assignment

Enhanced Role with Attributes

<role expertise="machine learning" tone="professional">
  You are a senior ML engineer with 10+ years 
  of experience in predictive modeling
</role>

Detailed role with specific attributes

💡 Role Best Practices

• Be specific about expertise level (junior, senior, expert)
• Include relevant experience or background
• Specify communication style (casual, professional, academic)
• Consider the target audience for the AI's responses

Task Definition and Instructions

Separating Concerns: Task vs Instructions

<task> - The "What"

<task>
  Analyze customer feedback and identify 
  key improvement opportunities
</task>

High-level objective or goal

<instructions> - The "How"

<instructions>
  1. Categorize feedback by sentiment
  2. Extract specific pain points
  3. Prioritize by frequency mentioned
  4. Suggest actionable solutions
</instructions>

Step-by-step methodology

Data Integration: Beyond Text

Images

<img 
  src="chart.png" 
  alt="Sales data visualization"
  style="detailed-analysis"
/>

Embed images with contextual styling

Documents

<document 
  src="report.pdf"
  pages="1-5"
  focus="executive-summary"
/>

Include specific document sections

Structured Data

<table 
  src="data.csv"
  columns="name,score,category"
  limit="100"
/>

Process tabular data efficiently

Examples and Output Formatting

Powerful Example Structure

<example type="few-shot">
  <input>Customer Review: "The app crashes every time I try to upload photos"</input>
  <output>
    Category: Technical Issue
    Sentiment: Negative
    Priority: High
    Suggested Action: Fix photo upload bug, implement crash reporting
  </output>
</example>

<example type="few-shot">
  <input>Customer Review: "Love the new dashboard design, so intuitive!"</input>
  <output>
    Category: UI/UX Feedback
    Sentiment: Positive
    Priority: Low (continue current approach)
    Suggested Action: Document design patterns for consistency
  </output>
</example>

Example Types:

type="few-shot"

Multiple training examples

type="demonstration"

Single detailed example

Precise Output Control

Style Attributes

<output-format 
  style="bullet"
  length="concise"
  tone="professional"
>
  Provide insights in bullet points,
  maximum 5 points per category
</output-format>

Format Constraints

<output-format 
  format="json"
  schema="feedback-analysis"
>
  {
    "sentiment": "positive|negative|neutral",
    "category": "string",
    "priority": "high|medium|low"
  }
</output-format>

Advanced POML Features

Variables and Templating

<poml>
  <variable name="industry" value="healthcare" />
  <variable name="audience" value="executives" />
  
  <role>
    You are a {industry} analyst speaking to {audience}
  </role>
  
  <task>Create industry-specific insights</task>
</poml>

Reusable templates with dynamic content

Conditional Logic

<if condition="audience == 'technical'">
  <instructions>
    Include technical details and code examples
  </instructions>
</if>

<if condition="audience == 'executive'">
  <instructions>
    Focus on business impact and ROI
  </instructions>
</if>

Dynamic prompts based on conditions

CSS-like Styling System

Separate content from presentation using POML's powerful styling capabilities.

Style Definitions

<style>
  .verbose { 
    detail-level: high;
    explanation-depth: comprehensive;
  }
  
  .concise {
    word-limit: 100;
    format: bullet-points;
  }
</style>

Apply Styles

<output-format class="concise">
  Summarize the key findings
</output-format>

<example style="verbose">
  Provide detailed explanation
</example>

POML Best Practices: Professional Standards

POML Best Practices: Professional Prompt Engineering

Master the art of POML with industry-proven practices that maximize prompt effectiveness, maintainability, and team collaboration.

💡 These practices are derived from Microsoft's internal testing and community feedback from hundreds of POML implementations.

1. Modular Design Principles

✅ Do: Atomic Components

<!-- Reusable role component -->
<role id="data-scientist">
  You are a senior data scientist with expertise 
  in machine learning and statistical analysis
</role>

<!-- Reusable output format -->
<output-format id="executive-summary">
  Provide insights in executive summary format:
  - Key findings (3-5 bullet points)
  - Impact assessment
  - Recommended actions
</output-format>

Create reusable components with unique IDs for consistency across prompts.

❌ Avoid: Monolithic Blocks

<task>
  You are a data scientist. Analyze the sales data 
  and provide insights. Focus on trends, anomalies,
  and predictions. Format as executive summary with
  bullet points and include methodology details and 
  statistical significance and confidence intervals...
</task>

Mixing multiple concerns in a single tag makes prompts hard to maintain and reuse.

2. Logical Component Organization

Recommended Structure Order:

1. Variables & Imports - Define reusable values
2. Role Definition - Establish AI personality
3. Context - Background information
4. Task - Primary objective
5. Instructions - Detailed methodology
6. Data/Examples - Supporting materials
7. Output Format - Response structure
8. Constraints - Limitations and rules

<poml>
  <!-- Variables -->
  <variable name="analysis_type" value="quarterly_review" />
  
  <!-- Role -->
  <role expertise="business-analysis" experience="senior">
    You are a senior business analyst
  </role>
  
  <!-- Context -->
  <context>
    Analyzing Q3 performance data for strategic planning
  </context>
  
  <!-- Task -->
  <task>Conduct {analysis_type} performance analysis</task>
  
  <!-- Instructions -->
  <instructions>
    1. Identify key performance indicators
    2. Compare against previous quarters
    3. Highlight significant trends
  </instructions>
  
  <!-- Data -->
  <table src="q3_data.csv" />
  
  <!-- Output Format -->
  <output-format style="executive-summary" />
  
  <!-- Constraints -->
  <constraints>
    Maximum 2 pages, focus on actionable insights
  </constraints>
</poml>

3. Version Control and Collaboration

📝 Documentation Standards

<poml version="2.1" author="team-ai">
  <!-- 
    Purpose: Customer feedback analysis
    Last modified: 2025-01-15
    Dependencies: sentiment-model-v3
    Changelog: Added multi-language support
  -->
  
  <metadata>
    <title>Customer Feedback Analyzer</title>
    <description>
      Analyzes customer feedback for sentiment,
      topics, and actionable insights
    </description>
    <tags>feedback, sentiment, nlp</tags>
  </metadata>

Include metadata and clear documentation for team collaboration.

🔄 Component Versioning

<!-- Import stable components -->
<import src="roles/analyst-v2.poml" />
<import src="formats/executive-summary-v1.poml" />

<!-- Use semantic versioning -->
<role ref="analyst-v2">
  <specialization>financial-markets</specialization>
</role>

<output-format ref="executive-summary-v1" 
               length="concise" />

Reference versioned components to maintain stability across updates.

4. Testing and Validation Strategies

A/B Testing POML Prompts

Version A: Detailed Role

<role expertise="marketing" experience="10-years">
  You are a senior marketing strategist with 
  extensive experience in B2B campaigns
</role>

Version B: Concise Role

<role>
  You are a marketing expert
</role>

Testing Metrics:

• Response relevance and accuracy
• Consistency across multiple runs
• Time to generate response
• User satisfaction scores

5. Performance Optimization

🚀 Token Efficiency

• Use concise, precise language
• Remove redundant instructions
• Leverage style attributes vs. verbose descriptions
• Use refs instead of repeating content

style="concise" vs.
"Please be brief and to the point"

⚡ Caching Strategies

• Cache compiled POML templates
• Reuse role and format definitions
• Store frequently used examples
• Implement template warming

Cache hit rate target: >85%

📊 Monitoring

• Track prompt performance metrics
• Monitor token usage patterns
• Alert on error rate spikes
• Log successful patterns

SLA target: <2s response time

6. Common Pitfalls to Avoid

🚫 Over-Engineering

Don't create unnecessary complexity. Simple tasks don't need elaborate POML structures.

Too Complex:

15+ nested components for basic translation

Right Size:

Simple role + task + output format

🚫 Inconsistent Naming

Establish naming conventions and stick to them across your organization.

Inconsistent:

id="DataAnalyst"

id="content_writer"

id="Marketing-Specialist"

Consistent:

id="data-analyst"

id="content-writer"

id="marketing-specialist"

🚫 Ignoring Context Length

Monitor total prompt length including data. Large POML templates can exceed model context windows.

Best Practice: Use limit attributes on data sources and implement pagination for large datasets.

7. Team Collaboration Excellence

🤝 Code Review Process

• Review POML structure and organization
• Validate example quality and diversity
• Check for reusable component opportunities
• Ensure consistent naming conventions
• Test with edge cases and error conditions

📚 Knowledge Sharing

• Maintain shared component library
• Document successful patterns
• Share performance optimization tips
• Create domain-specific templates
• Regular POML best practices sessions

🏆 Success Metrics

40%

Faster Development

65%

Fewer Conflicts

30%

Higher Productivity

Quick Reference Checklist

Before Deployment ✅

☐ Components are atomic and reusable
☐ Logical structure order maintained
☐ Consistent naming conventions
☐ Metadata and documentation added
☐ Examples are diverse and high-quality
☐ Output format is well-defined

Post-Deployment 📊

☐ Performance metrics monitored
☐ User feedback collected
☐ A/B test results analyzed
☐ Token usage optimized
☐ Error rates within SLA
☐ Team knowledge sharing completed

Industry-Specific Examples

See how prompting can be applied in different professional fields.

Education

Design a Lesson Plan

Act as a high school history teacher. Create a lesson plan for a 1-hour class on the main causes of World War I. The plan should include learning objectives, key terms, a 15-minute lecture outline, a 20-minute group activity, and a simple assessment question.

Exploring Different Models

Techniques in this guide apply broadly, but different models have unique strengths. Explore platforms like Hugging Face (for open models) and specialized models like Llama 3.

Hugging Face & Llama

Hugging Face is a hub for finding and testing models. Llama, by Meta, is a powerful open-source model you can run locally for greater control.

Act as Llama 3, the latest open-source model from Meta. Briefly introduce yourself and highlight two of your key improvements over previous versions.

Best Practices & Guidelines

✅ Do's

Be Clear & Specific: Ambiguity is the enemy. Clearly state the task, context, and desired format.
Provide Examples: Guide the model's output structure and style with concrete examples.
Use Delimiters: Use markers like ### or --- to separate instructions from content.
Iterate & Test: Your first prompt is rarely your best. Refine and test systematically.
Specify Output Format: Be explicit about desired format (JSON, bullet points, table, etc.)

❌ Don'ts

Don't Be Vague: Avoid unclear instructions like "make it better" or "analyze this".
Don't Overload: Don't cram multiple unrelated tasks into one prompt.
Don't Assume Context: The model doesn't remember previous conversations unless provided.
Don't Skip Testing: Always test prompts with different inputs to ensure consistency.
Don't Ignore Bias: Be aware of potential biases in model responses.

🔧 Advanced Optimization Techniques

Temperature Control

Lower temperature (0.1-0.3) for factual tasks, higher (0.7-0.9) for creative work.

Token Management

Keep prompts concise but complete. Balance detail with efficiency.

Prompt Chaining

Break complex tasks into sequential prompts for better accuracy.

Error Handling

Include instructions for handling edge cases and uncertain scenarios.

📊 Evaluation & Testing Framework

Accuracy

Does the output correctly answer the question or complete the task?

Consistency

Does the model produce similar outputs for similar inputs?

Relevance

Is the response directly related to the prompt and context?

Pro Tip: Create a test suite with diverse inputs to validate your prompts across different scenarios and edge cases.

🚨 Common Pitfalls to Avoid

Leading Questions

❌ "Isn't Python the best programming language for AI?"

✅ "Compare Python, R, and Julia for AI development."

Instruction Conflicts

❌ "Be brief but provide detailed explanations."

✅ "Provide a summary followed by detailed explanation."

Prompt Injection

Be aware of user inputs that might override your instructions.

Context Window Limits

Monitor token usage to avoid truncated responses.

Risks & Caution

Be mindful of common pitfalls:

Bias: Models can reflect biases from their training data. Scrutinize outputs.
Hallucination: Models can invent facts. Always verify critical information.
Privacy: Never include sensitive personal or corporate data in your prompts.