Few-Shot Prompting

Week 2: Techniques in Prompt Engineering

Few-Shot Prompting | Become a Prompt Engineer

Few-shot Prompting: Leveraging Examples for Better Responses

Enhance the accuracy and relevance of your AI model's outputs by strategically providing examples within your prompts.

Understanding Few-shot Prompting

Few-shot prompting is a technique where you provide a few examples within your prompt to guide the AI in generating more accurate and relevant responses. This method helps the model understand the desired pattern or structure by showing it how to respond through examples.

Few-shot prompting improves AI response accuracy by providing relevant examples within the prompt, helping the model align with specific task requirements.

Advantages of Few-shot Prompting

  • Improved Accuracy: Examples help the model align with expected outputs.
  • Contextual Guidance: Examples provide context for better task understanding.
  • Consistency: Ensures responses follow a consistent style or format.
  • Flexibility: Adaptable to various tasks, from text generation to classification.

Key Elements of Effective Few-shot Prompts

  1. Relevance: Choose examples that closely match the desired output.
  2. Diversity: Provide varied examples to cover different aspects of the task.
  3. Clarity: Ensure that your examples are clear and unambiguous.
  4. Explicit Instructions: Clearly state the task and how the examples should be interpreted.

Effective few-shot prompts use relevant, diverse examples with clear instructions to guide the AI in producing appropriate responses.

Applying Few-shot Prompting

Let's examine how to construct effective few-shot prompts for different tasks:

Example: Sentiment Analysis

This example demonstrates how to use few-shot prompting for sentiment classification of customer reviews.


from openai import OpenAI

client = OpenAI()

def get_ai_response(prompt):
    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=150
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Your prompt here
user_prompt = """Classify the sentiment of the following customer reviews as 'Positive,' 'Negative,' or 'Neutral.' Here are a few examples:

1. I absolutely love this product! It exceeded my expectations. -> Positive
2. The item arrived late and was damaged. Very disappointed. -> Negative
3. The product is okay, but I've seen better. Not too impressed. -> Neutral

Now, classify the sentiment of the following review:
The packaging was nice, but the product didn't work as advertised."""

print("\nGenerating AI response...")
response = get_ai_response(user_prompt)
print("\nAI Response:")
print(response)
                    

Analyzing the Prompt

  • Relevance: The examples directly match the task of sentiment classification.
  • Clarity: The prompt is straightforward, making it easy for the AI to follow.
  • Structure: The examples are well-structured, showing both the review and the expected sentiment.

Guidelines for Constructing Few-shot Prompts

  1. Select examples that represent the task you want the AI to perform.
  2. Use a variety of examples to cover different cases and edge scenarios.
  3. Be explicit in your instructions to minimize ambiguity.
  4. Test your prompt with different sets of examples to ensure consistency.
  5. Adjust the number of examples based on the complexity of the task.

Practice Exercises

Now it's your turn to craft few-shot prompts. Try these exercises to hone your skills:

Exercise 1: Text Summarization

Create a few-shot prompt that asks the AI to summarize articles while maintaining key details.


from openai import OpenAI

client = OpenAI()

def get_ai_response(prompt):
    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=100
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Your prompt here
user_prompt = """Summarize the following news articles in 50 words or less. Here are some examples:

1. A new study reveals that regular exercise can significantly reduce the risk of chronic diseases. Researchers found that even moderate physical activity has a positive impact on overall health. -> Regular exercise reduces chronic disease risk, with moderate activity improving health, according to a study.

2. The government announced a new policy aimed at reducing carbon emissions. The policy includes incentives for businesses to adopt green technologies and penalties for high polluters. -> New government policy targets carbon emission reduction with green tech incentives and penalties for polluters.

Now, summarize the following article:
Scientists have discovered a new species of bird in the Amazon rainforest. The bird is known for its vibrant colors and unique song. Conservationists are urging immediate protection of its habitat to prevent extinction."""

print("\nGenerating AI response...")
response = get_ai_response(user_prompt)
print("\nAI Response:")
print(response)
                        

Exercise 2: Customer Support Responses

Craft a few-shot prompt that guides the AI to generate polite and helpful responses to customer inquiries.


from openai import OpenAI

client = OpenAI()

def get_ai_response(prompt):
    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "user", "content": prompt}
            ],
            temperature=0.7,
            max_tokens=100
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Your prompt here
user_prompt = """Generate a polite and helpful response to customer inquiries. Here are some examples:

1. Customer: I'm having trouble logging into my account. Can you help me? -> Response: I'm sorry to hear you're having trouble logging in. Let's get this sorted out for you. Please try resetting your password using the 'Forgot Password' link, and let me know if you need further assistance.

2. Customer: The product I received was damaged. What should I do? -> Response: I apologize for the inconvenience caused by the damaged product. Please contact our support team with your order details, and we'll arrange for a replacement or refund as quickly as possible.

Now, generate a response to the following inquiry:
Customer: Can you provide more information about your return policy?"""

print("\nGenerating AI response...")
response = get_ai_response(user_prompt)
print("\nAI Response:")
print(response)
                        

Research: Language Models are Few-Shot Learners

This groundbreaking OpenAI paper, published in 2020, introduced GPT-3 and popularized the concept of few-shot learning in large language models. It demonstrated that scaling up model size can lead to significant improvements in performance and versatility.

Read the full paper: Language Models are Few-Shot Learners

Key Takeaways

  • Few-shot Learning: GPT-3 can perform various tasks with very few examples, often matching or exceeding fine-tuned models.
  • In-context Learning: The model learns to perform tasks from examples in the input context, without weight updates.
  • Scaling Laws: Performance improves predictably with model size and compute, following power-law relationships.
  • Versatility: Strong performance across translation, question-answering, and basic math tasks.
  • Emergent Abilities: New capabilities emerge in larger models that aren't present in smaller versions.

Impact on AI Research

This paper significantly influenced AI research by demonstrating the potential of large-scale language models as versatile, general-purpose AI systems.

  • Scaling up language models can lead to qualitative leaps in performance and capabilities.
  • Large language models can be highly versatile, potentially serving as general-purpose AI systems.
  • Few-shot learning could reduce the need for task-specific fine-tuning and large labeled datasets.

Ethical Considerations

The paper raised important ethical considerations that continue to be relevant in AI development today.

  • Potential biases in model outputs
  • Environmental impact of large-scale AI training
  • Risks of misuse for generating misleading information
  • Privacy concerns related to training data

This research laid the groundwork for many subsequent developments in AI, including the few-shot prompting techniques we're exploring in this course.

Summary

Few-shot prompting is a technique that uses examples within prompts to guide AI models in generating more accurate and relevant responses. By providing clear, diverse, and relevant examples, you can improve the model's understanding of the task and the desired output format. Practice and refinement of your few-shot prompts can significantly enhance the effectiveness of your interactions with AI models across various applications.