Few-shot Prompts

Few-shot prompting is a powerful technique where you provide the model with a small set of examples demonstrating the desired task before asking it to perform a similar task. This approach helps the model understand patterns and expectations through demonstration.

Key Concepts

• Learning from Examples: The model learns from provided examples
• Pattern Recognition: Examples demonstrate desired patterns and formats
• Consistent Structure: Examples follow a uniform format
• Implicit Rules: Rules are demonstrated rather than explicitly stated

Best Practices

1. Example Selection

   • Choose diverse, representative examples
   • Include edge cases when relevant
   • Maintain consistent quality across examples
   • Use realistic, accurate content

2. Structure and Format

   • Maintain consistent formatting across examples
   • Use clear separators between examples
   • Follow input-output pairs pattern
   • Keep example format identical to the target task

3. Example Quantity

   • Use 2-5 examples for basic tasks
   • Include more examples for complex patterns
   • Balance comprehensiveness with conciseness
   • Consider model context window limitations

Examples

Basic Format

Input: "The food was delicious"
Output: Positive

Input: "The service was terrible"
Output: Negative

Input: "The restaurant was okay"
Output: Neutral

Input: "The ambiance was fantastic but the prices were too high"
Output: [Your task: classify the sentiment]

Complex Pattern

Text: "Red roses bloom in spring"
Analysis:
- Subject: roses
- Color: red
- Action: bloom
- Time: spring

Text: "Tall trees sway in the wind"
Analysis:
- Subject: trees
- Attribute: tall
- Action: sway
- Condition: in the wind

Text: "The old car rusts in the garage"
Analysis:
[Your task: complete the analysis]

Common Pitfalls

1. Inconsistent Examples

   • Mixed formats across examples
   • Inconsistent complexity levels
   • Varying levels of detail
   • Contradictory patterns

2. Poor Example Selection

   • Too similar examples
   • Unrepresentative cases
   • Overly simplistic examples
   • Biased example sets

3. Format Issues

   • Unclear separation between examples
   • Inconsistent structure
   • Missing crucial elements
   • Confusing layout

Use Cases

1. Format Conversion

   • Data transformation
   • Style adaptation
   • Template filling
   • Format standardization

2. Analysis Tasks

   • Pattern recognition
   • Structure extraction
   • Content classification
   • Feature identification

3. Creative Tasks

   • Writing style matching
   • Content generation
   • Format-specific creation
   • Pattern-based outputs

When to Use Few-shot

Few-shot prompting is ideal when:

• The task requires specific formatting
• You need consistent output structure
• The pattern is best learned by example
• Zero-shot attempts produce inconsistent results

When to Consider Alternatives

Consider other techniques when:

• Examples would take too much context space
• The task is simple and well-defined
• Pattern demonstration isn't necessary
• Zero-shot prompting works sufficiently

Tips for Success

1. Example Design

   • Create clear, representative examples
   • Include variety while maintaining consistency
   • Demonstrate key variations
   • Show edge cases when relevant

2. Format Optimization

   • Use clear, consistent separators
   • Maintain identical structure across examples
   • Include all relevant components
   • Make patterns easily identifiable

3. Quality Control

   • Test with different inputs
   • Verify pattern recognition
   • Check for consistency
   • Validate edge cases

Advanced Techniques

1. Dynamic Examples

   • Adapt examples to specific contexts
   • Use domain-specific instances
   • Scale complexity progressively
   • Include relevant variations

2. Hybrid Approaches

   • Combine with explicit instructions
   • Mix with other prompt types
   • Include explanatory elements
   • Add context when needed