Few-Shot Learning

Provide examples to guide the AI's understanding and response pattern.

Few-Shot Learning involves providing the AI with a small number of examples to demonstrate the desired pattern, format, or style of response. This technique is highly effective for teaching specific formats, tones, or approaches without extensive fine-tuning.

Key Features

Pattern recognition through examples
Consistent output formatting
Quick adaptation to new tasks
No model retraining required
Flexible and easy to implement

Pros and Cons

Advantages

Quick to implement and understand
Excellent for format consistency
Works well with limited data
Highly adaptable to different tasks
No technical expertise required

Considerations

Requires good examples to be effective
May not work for completely novel tasks
Can be limited by example quality
May need multiple examples for complex patterns
Examples consume token space

Framework Template

Here are examples of similar requests and high-quality responses:

**Example 1:**
Request: [Similar prompt example]
Response: [High-quality structured response]

**Example 2:** 
Request: [Another similar example]
Response: [Another well-structured response]

**Your Request:**
[Your prompt content]

Following the pattern above, please provide a detailed response.

Example: Email Response Formatting

Prompt

Example 1: Inquiry: 'When will my order arrive?' Response: 'Thank you for your inquiry. Your order #12345 is currently being processed and will ship within 2-3 business days. You'll receive tracking information via email once it's dispatched.' Example 2: Inquiry: 'Can I return this item?' Response: 'Absolutely! You can return your item within 30 days of purchase. Please visit our returns page or contact customer service for a return label.' Now respond to: 'Is this product available in other colors?'

Expected Response

Thank you for your interest! Yes, this product is available in several colors including navy blue, forest green, and charcoal gray. You can view all available colors on the product page or contact our team for specific color availability.

More Examples

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Email Response Formatting

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Creative Writing Patterns

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Example 1: Genre: Horror Opening: 'The old house had been empty for decades, but tonight, warm light flickered in the upstairs window.' Example 2: Genre: Romance Opening: 'She never believed in second chances until she saw him standing in their old coffee shop, looking exactly the same after ten years.' Now write an opening for: Genre: Sci-Fi

Data Extraction and Classification

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Example 1: Text: 'John Smith, age 32, software engineer at TechCorp, living in San Francisco' Extracted: Name: John Smith | Age: 32 | Job: Software Engineer | Company: TechCorp | Location: San Francisco Example 2: Text: 'Maria Garcia, 28-year-old marketing manager from Austin, works at StartupXYZ' Extracted: Name: Maria Garcia | Age: 28 | Job: Marketing Manager | Company: StartupXYZ | Location: Austin Now extract from: 'David Chen, 35, data scientist at BigTech Inc, based in Seattle'

Code Generation Patterns

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Example 1: Task: Create a function to calculate area of rectangle Code: ```python def rectangle_area(length, width): return length * width ``` Example 2: Task: Create a function to check if number is even Code: ```python def is_even(number): return number % 2 == 0 ``` Now create: Task: Create a function to find maximum of two numbers

Translation and Language Tasks

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Example 1: English: 'Good morning, how are you?' Formal Spanish: 'Buenos días, ¿cómo está usted?' Casual Spanish: '¡Hola! ¿Cómo estás?' Example 2: English: 'Thank you very much for your help' Formal Spanish: 'Muchas gracias por su ayuda' Casual Spanish: '¡Gracias por tu ayuda!' Now translate: 'Can you help me find the restaurant?'

Best Use Cases

Content formatting and styling

Creative writing patterns

Customer service responses

Data extraction and classification

Translation and language tasks

Code generation patterns

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