Few-Shot Prompting
technique technique
Learning with few examples in the prompt
Supported languages
Few-Shot Prompting is a technique that improves LLM responses by including some demonstrative examples in the prompt. The model learns the desired pattern or format from these examples and applies it to new inputs without additional training.
Concepts
in-context-learningdemonstration-examplesexample-selectionshot-countprompt-templatesoutput-formatting
Pros and Cons
Ventajas
- + Significant improvement without training
- + Precise control over output format
- + Works with any modern LLM
- + Easy to iterate and adjust
- + Ideal for classification and extraction tasks
- + Reduces instruction ambiguity
Desventajas
- - Consumes valuable context tokens
- - Requires high-quality examples
- - Can introduce biases from examples
- - Limited by model context size
- - Example selection greatly impacts results
Casos de Uso
- Text classification with specific categories
- Entity and structured data extraction
- Translation with specific style
- Content generation with defined format
- Custom sentiment analysis