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Overview

In this lesson, you will learn:

• How to guide AI using examples (few-shot prompting)
• How to use roles/personas to control output style and tone
• Beginner-friendly strategies to make prompts more precise and effective
• How these techniques improve the quality and relevance of AI outputs

By the end, you will know how to write prompts that consistently produce the outputs you want.

💡 Key Concepts

• Few-Shot Prompting: Providing 2–5 examples in your prompt to show AI what output you expect.
• Role-Based Prompting: Assigning a role or persona to AI to guide tone, style, or expertise.
• Clarity & Consistency: Clear instructions and examples make AI outputs predictable and useful.
• Iterative Improvement: Few-shot and role prompts are often refined over multiple attempts for optimal results.

🧠 Concept Explanation

1. Few-Shot Prompting

Few-shot prompting gives the AI examples of the desired behavior so it can mimic them.

Why it helps:

• AI “learns” the pattern from examples
• Reduces errors in classification, summarization, or style reproduction

Example – Sentiment Classification (3-shot):

Review 1: "The food was amazing!" -> Positive
Review 2: "Service was very slow." -> Negative
Review 3: "The ambiance was cozy." -> Positive
Classify: "The movie was boring."  
Output: Negative

• The examples show AI exactly how to classify reviews.
• Beginner tip: Start with 2–3 examples for clarity.

2. Role-Based Prompting

Role-based prompting assigns a persona or role to AI. This guides tone, style, and perspective.

Why it helps:

• Makes responses consistent with your goals
• Useful for storytelling, teaching, professional advice, or customer support

Example – Role Prompt:

Role: You are a friendly math teacher.  
Task: Explain the Pythagorean theorem to a 10-year-old.  
Output: Simple, clear, beginner-friendly explanation.

• Roles can be teacher, coach, doctor, chef, scientist, historian, etc.
• Combine with examples (few-shot) for even better control.

3. Combining Few-Shot and Role

You can combine these techniques for maximum guidance:

Example – Translation Task:

Role: You are a professional translator.  
Examples:
1. "Hello" -> "Bonjour"
2. "Good morning" -> "Bonjour"
3. "Thank you" -> "Merci"
Translate: "See you tomorrow"  
Output: "À demain"

• AI now understands role, task, and pattern from examples.
• Beginner tip: Use short, clear examples in the same format.

4. Beginner Tips

• Keep examples simple and consistent
• Assign roles that fit your task purpose
• Use iteration: tweak role, instructions, or examples based on output
• Combine context + role + few-shot for structured and reliable results

🧩 Practical Examples

1. Few-Shot Summarization

Example 1: "The cat chased the mouse." -> "A cat chased a mouse."
Example 2: "It rained all day." -> "It rained throughout the day."
Summarize: "The sun set behind the mountains."  
Output: "The sun set over the mountains."

2. Role-Based Advice

Role: You are a career coach.
Task: Give 3 productivity tips for college students.
Output: Numbered list of tips in beginner-friendly language.

3. Combined Few-Shot + Role

Role: You are a friendly chef.
Example 1: "Chop onions finely." -> "Slice the onions into small pieces."
Example 2: "Boil water for pasta." -> "Bring water to a boil before adding pasta."
Instruction: "Grill chicken breast."  
Output: Beginner-friendly step-by-step instructions.

⚙️ Beginner Tools

• ChatGPT / OpenAI Playground: Paste few-shot examples and roles.
• Replit / Jupyter Notebook: For simple scripts combining multiple examples in prompts.
• Voice assistants: Test simple role-based prompts verbally.

🧭 Step-by-Step Beginner Activity

1. Pick a task (e.g., “summarize a paragraph”).
2. Write 2–3 examples showing how AI should respond.
3. Assign a role that fits the output style (teacher, coach, expert).
4. Add task instruction clearly.
5. Input prompt into AI and review output.
6. Adjust examples or role if the output is not as expected.

📝 Exercises

1. Create a few-shot prompt to classify tweets as positive or negative.
2. Assign AI a role (e.g., “friendly nutritionist”) and ask for beginner-level health tips.
3. Combine few-shot examples with a role to explain a science topic in 3 steps.
4. Experiment by changing role or examples and compare outputs.

🔍 Summary & Key Takeaways

• Few-shot prompting guides AI using examples of desired outputs.
• Role-based prompting shapes tone, style, and perspective.
• Combining few-shot + role produces more reliable and tailored responses.
• Beginners should start small (2–3 examples) and iterate to refine outputs.
• Mastering these techniques is a critical step in practical prompt engineering.