AI Prompt Engineering: Complete Course (Basics to Advanced) : Module: 2- Basic Prompting Techniques

 

Chapter 2: Basic Prompting Techniques

2.1 Zero-Shot Prompting

Overview: Zero-shot prompting involves giving a model a task without providing examples, relying on its pre-trained knowledge to generate a response.

Example:

• Prompt: "Summarize the plot of 'Pride and Prejudice' in 50 words."
• Expected Output: A concise summary of the novel, leveraging the model's understanding without prior examples.

Use Case: Quick answers for general knowledge tasks, like summarizing texts or answering factual questions.

Key Points:

• Works best with well-trained, general-purpose models like Grok.
• No need for training data, making it efficient for one-off tasks.
• May struggle with niche or highly specific tasks.

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2.2 Few-Shot Prompting

Overview: Few-shot prompting provides a few examples of the desired task within the prompt to guide the model's response.

Example:

• Prompt:
  text

  Classify the sentiment of the following sentences as positive, negative, or neutral.

  1. I love this product! It's amazing. -> Positive

  2. This is the worst experience ever. -> Negative

  3. The item is okay, nothing special. -> Neutral

  New sentence: The service was fantastic and quick! -> ?
• Expected Output: Positive

Use Case: Tasks requiring specific formats or styles, like classification or structured outputs.

Key Points:

• Improves accuracy by setting context with examples.
• Requires careful selection of representative examples.
• Balances between zero-shot (no examples) and fine-tuning (extensive training).

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2.3 Role-Playing Prompts

Overview: In role-playing prompts, the model is instructed to adopt a specific persona or role to tailor its responses.

Example:

• Prompt: "You are a pirate captain. Describe your latest adventure on the high seas in 100 words."
• Expected Output: A narrative in the voice of a pirate, e.g., "Arr, me hearties! Last moon, we sailed the Crimson Tide, chasin’ a Spanish galleon loaded with gold. Storms battered our ship, but I, Captain Blackthorn, rallied the crew. We boarded the foe at dawn, swords clashin’! We nabbed the treasure, but a kraken stirred below. Narrowly escapin’, we hid the loot on Skull Island. More adventures await!"

Use Case: Creative writing, customer-facing roles, or simulating expert perspectives.

Key Points:

• Enhances engagement by adopting a specific tone or perspective.
• Requires clear role definition to avoid inconsistent responses.
• Fun for storytelling or persona-based interactions.

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2.4 Instructional Prompts

Overview: Instructional prompts provide explicit, step-by-step directions to achieve a specific output format or task.

Example:

• Prompt: "Write a 3-step guide to reset a password. Use bullet points and keep each step under 20 words."
• Expected Output:
  • Visit the login page and click "Forgot Password."
  • Enter your email to receive a reset link.
  • Follow the link to set a new password.

Use Case: Technical writing, tutorials, or tasks requiring structured outputs.

Key Points:

• Ensures clarity and adherence to specific formats.
• Works well for procedural or task-oriented queries.
• Can reduce ambiguity in complex tasks.

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2.5 Real-Life Example: Customer Service Chatbot Optimization

Overview: Prompting techniques can optimize chatbots for customer service by improving response relevance and tone.

Example:

• Scenario: A chatbot handles refund requests.
• Prompt (Few-Shot + Role-Playing):
  text

  You are a polite customer service agent. Respond to refund requests with empathy and clear instructions.

  Example 1:

  User: I want a refund for my defective item.

  Response: I'm so sorry to hear that! Please share your order number, and I'll guide you through the refund process.

  Example 2:

  User: My product broke after one use, refund me!

  Response: I apologize for the inconvenience. Could you provide your order details? We'll process your refund promptly.

  New Request: The item I ordered doesn’t work. I need a refund.
• Expected Output: "I’m truly sorry for the issue! Please provide your order number, and I’ll assist with your refund process."

Use Case: Enhancing user experience in automated customer support systems.

Key Points:

• Combines few-shot and role-playing for consistent, empathetic responses.
• Reduces escalations by addressing user frustration effectively.
• Can be scaled for various industries (e.g., retail, tech support).

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2.6 Code Snippet: Implementing Few-Shot in Python

Overview: Few-shot prompting can be implemented programmatically using an API like xAI’s Grok API.

Code Snippet:

python

import requests

def few_shot_prompt(api_key, prompt):

url = "https://api.x.ai/grok"

headers = {"Authorization": f"Bearer {api_key}"}

data = {

"model": "grok",

"prompt": prompt,

"max_tokens": 100

}

response = requests.post(url, headers=headers, json=data)

return response.json().get("choices")[0].get("text")

# Example few-shot prompt

prompt = """

Classify the sentiment of the following sentences as positive, negative, or neutral.

1. I love this product! It's amazing. -> Positive

2. This is the worst experience ever. -> Negative

3. The item is okay, nothing special. -> Neutral

New sentence: The service was fantastic and quick! -> ?

"""

api_key = "your_api_key_here"

result = few_shot_prompt(api_key, prompt)

print(result) # Expected: Positive

Notes:

• Replace "your_api_key_here" with a valid xAI API key (see https://x.ai/api for details).
• Demonstrates how to structure a few-shot prompt for sentiment analysis.
• Requires error handling for API failures (see 2.8).

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2.7 Best Practices for Basic Techniques

• Clarity: Use concise, unambiguous language in prompts to reduce misinterpretation.
• Context: Provide enough context (e.g., examples in few-shot) to guide the model without overwhelming it.
• Iterate: Test and refine prompts based on output quality.
• Tone Consistency: Specify tone (e.g., formal, friendly) for role-playing or customer-facing prompts.
• Limit Scope: Narrow the task to avoid vague or off-topic responses.
• Example Selection: Choose diverse, representative examples for few-shot prompting.

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2.8 Exception Handling: Dealing with Ambiguous Outputs

Overview: Ambiguous outputs occur when the model misinterprets the prompt or lacks context.

Strategies:

• Rephrase Prompt: Simplify or add details to clarify intent.
  • Example: If "Summarize this article" yields vague results, try "Summarize the main points of this article in 3 sentences."
• Add Constraints: Specify word limits, formats, or tone to reduce ambiguity.
• Fallback Examples: Use few-shot prompting to anchor the model to desired outputs.
• Check Response: Programmatically validate outputs (e.g., check for keywords or structure) and retry if needed.

Example:

• Ambiguous Prompt: "Tell me about AI."
• Improved Prompt: "Explain the benefits of AI in healthcare in 100 words."

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2.9 Pros, Cons, and Alternatives

• Zero-Shot Prompting:
  • Pros: Fast, no setup, leverages model’s general knowledge.
  • Cons: Less accurate for specialized tasks, prone to misinterpretation.
  • Alternatives: Few-shot prompting or fine-tuning for better precision.
• Few-Shot Prompting:
  • Pros: Improves accuracy with minimal examples, flexible.
  • Cons: Requires crafting examples, may not scale for complex tasks.
  • Alternatives: Fine-tuning or chain-of-thought prompting.
• Role-Playing Prompts:
  • Pros: Engaging, tailors tone to audience, creative.
  • Cons: Risk of inconsistent persona if poorly defined.
  • Alternatives: Instructional prompts for more structured outputs.
• Instructional Prompts:
  • Pros: Structured, clear, ideal for procedural tasks.
  • Cons: Can be rigid, less creative.
  • Alternatives: Role-playing for more dynamic responses.

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