Module 7: Specialized Prompt Techniques

Advanced methods for complex reasoning and learning.

Introduction

Welcome to Module 7! You've mastered the fundamentals. Now, let's explore **Specialized Prompt Techniques** like Chain-of-Thought, Few-Shot, Zero-Shot, and Task Decomposition to tackle more complex challenges.

Learning Objectives:

  • Describe Chain-of-Thought (CoT) prompting.
  • Explain Few-Shot and Zero-Shot prompting.
  • Understand Task Decomposition for complex problems.
  • Apply these specialized techniques appropriately.

How this Connects: These advanced techniques build on core principles and prepare us for Module 8: Prompt Engineering for Different Tasks.

Core Content: Advanced Techniques

1. Chain-of-Thought (CoT) Prompting

Encourage the AI to "think step-by-step" or explain its reasoning. Great for math, logic, and reasoning tasks. Add phrases like "Let's think step by step."

Example CoT Reasoning (elicited by prompt):

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: Let's break this down. Roger starts with 5 balls. He buys 2 cans, and each can has 3 balls, so that's 2 * 3 = 6 balls. In total, he has 5 + 6 = 11 balls. The final answer is 11.

2. Zero-Shot Prompting

Asking the AI to perform a task *without* giving any examples in the prompt. Relies on the AI's pre-training. Best for simple, common tasks.

Example: Translate "Hello, world!" to Spanish.

3. Few-Shot Prompting

Providing a few examples (input/output pairs) within the prompt to show the AI the desired pattern or format. Useful for novel tasks or specific styles.

Example (Sentiment Analysis):

This movie was fantastic! // Positive I didn't enjoy the book. // Negative The weather is okay today. // Neutral That concert was absolutely amazing! // [AI completes: Positive]

Comparison:

When might you choose Few-Shot over Zero-Shot? (Hint: Novelty? Specificity needed?)

4. Task Decomposition

Breaking down a large, complex task into smaller, sequential sub-tasks and prompting the AI for each step. Makes complex goals manageable.

Example (Essay Outline): Start with a prompt for themes, use themes to prompt for section headings, use headings to prompt for supporting points, etc.

Practical Examples: Choosing Techniques

Goal: Solve a logic puzzle.

Technique: Chain-of-Thought

Prompt Idea: ... Which box should you pick and why? Let's think step by step.

Goal: Categorize customer feedback.

Technique: Few-Shot

Prompt Idea: Provide examples like Input // Category then the final input.

Goal: Write a detailed report.

Technique: Task Decomposition

Prompt Idea: Separate prompts for identifying sections, describing impacts per section, summarizing strategies.

Choosing Wisely:

  • Simple/Known Task → Zero-Shot
  • Novel Task / Specific Format → Few-Shot
  • Reasoning/Math → Chain-of-Thought
  • Complex Goal → Task Decomposition

Check Your Understanding

1. Which technique involves providing several input-output examples within the prompt?

2. Adding "Let's think step by step" to a math problem prompt is an example of which technique?

3. Asking an AI to translate a sentence without providing any translation examples first is:

4. Breaking down "Plan a company event" into separate prompts for "Suggest themes," "Draft budget," and "Create schedule" uses which technique?

Hands-On Exercise: Applying Few-Shot

Goal: Extract the main keyword from short product descriptions.

Instructions:

  1. Create a Few-Shot prompt with 2-3 examples (Description // Keyword).
  2. Add a final description line for the AI to complete.
  3. Enter the full prompt below.

Expected Outcome:

Your prompt should contain clear, consistent examples and the final input line ready for AI completion (e.g., ending with `// Keyword:`).

Prompt Grading Section

Evaluate the Few-Shot prompt you created.

Evaluation Criteria (Self-Check):

1. Examples Provided (2+): Did you include at least 2 clear input/output examples?

2. Consistent Format: Do examples follow a consistent format (e.g., `Input // Output`)?

3. Final Input Ready: Is the final input line set up for AI completion?

4. Relevance: Are examples relevant to keyword extraction?

Suggestion for Improvement:

For Few-Shot, clarity and consistency in your examples are crucial for teaching the AI the desired pattern.

Module Summary

Fantastic! You've added specialized techniques to your toolkit.

Key Takeaways:

  • **Chain-of-Thought:** Encourages step-by-step reasoning.
  • **Zero-Shot:** No in-prompt examples needed.
  • **Few-Shot:** Provide examples to guide the AI.
  • **Task Decomposition:** Break complex tasks into smaller prompts.

Next Steps: Let's apply these techniques in Module 8: Prompt Engineering for Different Tasks.