LLM Classifier Skill
Capabilities
- Implement zero-shot classification with LLMs
- Design few-shot classification prompts
- Configure structured output for labels
- Implement confidence scoring
- Design classification taxonomies
- Handle multi-label classification
Target Processes
- intent-classification-system
- dialogue-flow-design
Implementation Details
Classification Patterns
- Zero-Shot: No examples, description-based
- Few-Shot: Example-based classification
- Structured Output: JSON schema for labels
- Chain-of-Thought: Reasoning before classification
- Ensemble: Multiple prompts/models
Configuration Options
- LLM model selection
- Label descriptions
- Example selection strategy
- Output format specification
- Confidence calibration
Best Practices
- Clear label descriptions
- Representative examples
- Consistent output format
- Calibrate confidence scores
- Test with edge cases
Dependencies
- langchain-core
- LLM provider