Prompt Engineering Guide¶

This guide covers prompt engineering techniques for getting the best results from AI models in the AI Assistant System.

Overview¶

Prompt engineering is the art and science of designing effective prompts to elicit desired responses from AI models. Well-crafted prompts can significantly improve the quality, relevance, and accuracy of model outputs.

Basic Principles¶

1. Be Clear and Specific¶

Vague prompts lead to vague responses. Be specific about what you want.

Poor Example:

Tell me about cars

Good Example:

Explain the key differences between electric vehicles and internal combustion engine vehicles, focusing on environmental impact, performance, and cost of ownership.

2. Provide Context¶

Give the model relevant background information to help it understand the request.

Example:

You are an expert financial advisor with 15 years of experience helping clients make investment decisions. Based on this expertise, analyze the following investment portfolio...

3. Set the Format¶

Specify the desired output format to get structured responses.

Example:

Analyze the following text and provide your response in JSON format with the following structure:
{
  "sentiment": "positive/negative/neutral",
  "confidence": 0.0-1.0,
  "key_points": ["point1", "point2", "point3"]
}

Advanced Techniques¶

1. Chain of Thought Prompting¶

Guide the model through step-by-step reasoning.

Example:

Solve this math problem step by step:
Problem: A store sells apples for $2 each and oranges for $3 each. If a customer buys 5 apples and 3 oranges, how much do they spend in total?

Step 1: Calculate the cost of apples
Step 2: Calculate the cost of oranges
Step 3: Add the costs together
Final Answer:

2. Few-Shot Learning¶

Provide examples to help the model understand the pattern.

Example:

Convert the following sentences to passive voice:

Example 1:
Active: The chef prepared the meal.
Passive: The meal was prepared by the chef.

Example 2:
Active: The team won the championship.
Passive: The championship was won by the team.

Now convert:
Active: The student wrote the essay.
Passive:

3. Role-Based Prompting¶

Assign a specific role to the model.

Example:

You are a senior software engineer at a tech company. Review this code for potential security vulnerabilities:

```python
def login(username, password):
    query = f"SELECT * FROM users WHERE username = '{username}' AND password = '{password}'"
    return execute_query(query)

Identify security issues and suggest improvements.

### 4. Constraint-Based Prompting

Set specific constraints on the response.

**Example:**

Write a product description for a smart home device with these constraints: - Maximum 100 words - Include at least 3 key features - Target audience: tech-savvy homeowners - Tone: professional but approachable

## Prompt Templates

### 1. Question Answering

Context: {context} Question: {question} Based on the provided context, answer the question concisely and accurately. If the context doesn't contain the answer, say "I don't have enough information to answer this question."

### 2. Text Summarization

Summarize the following text in {summary_length} sentences, focusing on the main points and key insights:

Text: {text}

Summary:

### 3. Code Generation

Generate {language} code to {task_description}. Include: 1. Clear comments explaining the logic 2. Error handling 3. Example usage 4. Time and space complexity analysis

Requirements: {requirements}

### 4. Data Extraction

Extract the following information from the text below and format it as JSON: - {field_1} - {field_2} - {field_3}

Text: {text}

JSON:

## Best Practices for Different Use Cases

### 1. Content Generation

**For Blog Posts:**

Write a blog post about {topic} with the following structure: 1. Catchy title 2. Introduction (2-3 sentences) 3. Main body (3-4 paragraphs) 4. Conclusion (1-2 sentences) 5. Call to action

Target audience: {audience} Tone: {tone} Word count: {word_count}

**For Product Descriptions:**

Create a compelling product description for {product} that: - Highlights 3 key benefits - Addresses 2 common pain points - Includes a social proof element - Ends with a clear call to action

Keep it under {word_count} words.

### 2. Analysis Tasks

**For Sentiment Analysis:**

Analyze the sentiment of the following text. Rate it on a scale of -1 (very negative) to 1 (very positive) and provide a brief explanation:

Text: {text}

Sentiment Score: [score] Explanation: [explanation]

**For Text Classification:**

Classify the following text into one of these categories: {categories}. Provide your reasoning:

Text: {text}

Category: [category] Reasoning: [reasoning]

### 3. Creative Tasks

**For Story Writing:**

Write a {genre} story with these elements: - Main character: {character_description} - Setting: {setting} - Conflict: {conflict} - Theme: {theme}

The story should be approximately {word_count} words and include a surprise ending.

**For Poetry:**

Write a {style} poem about {topic}. Use {metaphor_type} metaphors and maintain a {tone} tone throughout.

Length: {line_count} lines Rhyme scheme: {rhyme_scheme}

## Common Pitfalls and How to Avoid Them

### 1. Ambiguous Prompts

**Problem:**

Tell me about Python

**Solution:**

Explain the key features of Python programming language that make it suitable for data science, including specific libraries and their uses.

### 2. Leading Questions

**Problem:**

Don't you agree that our product is the best on the market?

**Solution:**

Evaluate our product against these criteria: performance, reliability, cost, and customer support. Provide an objective assessment.

### 3. Missing Context

**Problem:**

Fix this code: [code snippet]

**Solution:**

This Python function is intended to sort a list of dictionaries by the 'name' key, but it's not working correctly. Identify the issue and provide a corrected version:

[code snippet]

Expected behavior: The list should be sorted alphabetically by the 'name' key.

## Iterative Prompt Refinement

### 1. Start Simple

Begin with a basic prompt and evaluate the output.

### 2. Analyze Results

Identify what's missing or incorrect in the response.

### 3. Add Specificity

Refine the prompt with more specific instructions.

### 4. Test Variations

Try different phrasings and approaches.

### 5. Document What Works

Keep track of effective prompt patterns.

## Prompt Optimization Techniques

### 1. Temperature and Top-p Settings

For creative tasks:

Generate creative ideas for a marketing campaign. Use high creativity (temperature=0.9) and diverse options (top-p=0.95).

For factual tasks:

Provide accurate information about quantum computing. Use low creativity (temperature=0.2) and focused responses (top-p=0.5).

### 2. Token Management

For efficient token usage:

Summarize this article in exactly 50 words: [article]

### 3. Structured Prompts

Use clear section headers:

TASK: Analyze customer feedback CONTEXT: We received this feedback from a customer who used our product for 30 days FEEDBACK: [feedback text] ANALYSIS REQUIREMENTS: 1. Identify main concerns 2. Suggest improvements 3. Determine sentiment

## Measuring Prompt Effectiveness

### 1. Quality Metrics

- Accuracy: Does the response contain correct information?
- Relevance: Does it address the prompt appropriately?
- Completeness: Does it fully address all aspects of the prompt?
- Clarity: Is the response well-structured and easy to understand?

### 2. Efficiency Metrics

- Token usage: How many tokens were consumed?
- Response time: How long did it take to generate?
- Revision rate: How often do you need to revise the response?

### 3. A/B Testing

Compare different prompt versions:

Prompt A: [version 1] Prompt B: [version 2]

Test with [number] examples and compare: - Response quality ratings - User satisfaction scores - Task completion rates

## Tools and Resources

### 1. Prompt Management

Store and organize effective prompts:

```python
prompt_library = {
    "code_review": "You are a senior software engineer. Review this code for security vulnerabilities, performance issues, and maintainability...",
    "email_draft": "Draft a professional email with the following requirements...",
    "data_analysis": "Analyze this dataset and provide insights..."
}

2. Version Control¶

Track prompt versions and changes:

prompt_versions = {
    "v1.0": "Initial prompt version",
    "v1.1": "Added specific examples",
    "v1.2": "Improved formatting instructions",
    "v2.0": "Complete rewrite based on feedback"
}

Conclusion¶

Effective prompt engineering is a skill that improves with practice. Start with these principles and techniques, then refine your approach based on the specific needs of your application. Remember that the best prompts are:

Clear and specific
Rich in relevant context
Structured for the desired output
Tested and refined based on results

By following these guidelines, you can significantly improve the quality and consistency of responses from AI models in your applications.