Few-Shot Example Generation Skill

Capabilities

• Generate diverse few-shot examples
• Implement example selection strategies
• Optimize example ordering for performance
• Create dynamic example retrieval
• Design example formats for specific tasks
• Implement example quality validation

Target Processes

• prompt-engineering-workflow
• intent-classification-system

Implementation Details

Example Selection Strategies

1. Semantic Similarity: Select similar examples
2. MMR Selection: Diverse example selection
3. N-Gram Overlap: Lexical similarity
4. Random Sampling: Baseline selection
5. Length-Based: Control example sizes

Configuration Options

• Number of examples
• Selection algorithm
• Example format (input/output structure)
• Max token limits
• Example store backend

Best Practices

• Cover edge cases in examples
• Balance example diversity
• Optimize example ordering
• Test with varied inputs
• Monitor token usage

Dependencies

• langchain
• sentence-transformers (for semantic selection)