The Prompt Engineering Cookbook: A Comprehensive Guide
Introduction
Welcome to the Prompt Engineering Cookbook! This guide will walk you through various techniques and strategies to enhance your interactions with large language models (LLMs). Just as a skilled chef combines ingredients and techniques to create delicious dishes, a prompt engineer combines different prompting methods to elicit the best responses from AI models.
1. Basic Ingredients: Fundamental Concepts
In-Context Learning
Definition: The model's ability to temporarily learn from prompts
Key Point: An emergent property of large language models
Usage: Enables models to adapt to new tasks without fine-tuning
Emergent Abilities
Concept: Capabilities that appear in larger models but not in smaller ones
Example: Solving complex reasoning tasks
Importance: Guides the development of new prompting techniques
2. Appetizers: Simple Prompting Techniques
Zero-Shot Prompting
Recipe: Provide a task description without examples
Best For: Simple tasks or when the model has strong prior knowledge
Example: "Translate the following English text to French: [text]"
Few-Shot Prompting
Recipe: Give a few examples of the task before asking the model to perform it
Best For: More complex tasks or when additional context is helpful
Example:
"English: Hello
French: Bonjour
English: Goodbye
French: Au revoir
English: How are you?
French: [Let the model complete]"
3. Main Courses: Advanced Prompting Strategies
Chain-of-Thought (CoT) Prompting
Recipe: Ask the model to solve problems in intermediate steps
Best For: Complex reasoning tasks
Example: "Solve this math problem step by step: If a train travels 120 km in 2 hours, what is its average speed in km/h?"
Chain-of-Symbol (CoS) Prompting
Recipe: Use random symbols to assist with spatial reasoning in text
Best For: Tasks involving spatial or structural understanding
Example: "Arrange these words in alphabetical order, using symbols to represent their positions:
Apple (@), Banana (#), Cherry ($)"
Tree of Thoughts (ToT)
Recipe: Generate multiple possible next steps and evaluate them
Best For: Problems with multiple possible solution paths
Example: "Let's solve this puzzle step by step. At each step, we'll consider multiple options:
Puzzle: You have 8 coins, 7 of which are of equal weight, and 1 is slightly heavier. How can you identify the heavier coin in just two weighings using a balance scale?"
4. Side Dishes: Supplementary Techniques
Self-Consistency Decoding
Recipe: Perform several CoT rollouts and select the most common conclusion
Best For: Improving reliability in reasoning tasks
Usage: Run the same prompt multiple times and compare results
Generated Knowledge Prompting
Recipe: First prompt the model to generate relevant facts, then use those facts to complete the task
Best For: Tasks requiring specific background knowledge
Example: "First, list three key facts about photosynthesis. Then, use these facts to explain why leaves are green."
Prompt Chaining
Recipe: Combine multiple prompts in sequence for complex tasks
Best For: Breaking down complex tasks into manageable steps
Usage: Use the output of one prompt as input for the next
5. Desserts: Emerging Methods and Future Directions
Maieutic Prompting
Recipe: Prompt the model to explain parts of its explanation recursively
Best For: Enhancing logical consistency in complex reasoning
Future Potential: Improving AI's ability to provide coherent, multi-step explanations
Least-to-Most Prompting
Recipe: First list sub-problems, then solve them in sequence
Best For: Complex problems that can be broken down into smaller, manageable parts