Few-Shot Learning
Teaching AI new tasks by providing just a few examples in the prompt, enabling rapid adaptation without extensive retraining or datasets.
Definition
Few-shot learning shows AI how to perform a task by including several examples in the prompt, rather than requiring extensive training. The model recognizes the pattern and applies it to new inputs.
This technique enables customization without fine-tuning, making AI adaptable to specific formats, styles, and requirements through prompt engineering alone.
Why It Matters
Few-shot learning democratizes AI customization. Anyone can teach the model their preferred format or approach with a few well-chosen examples.
This capability makes AI practical for niche applications where formal training data doesn't exist.
Examples in Practice
A few-shot prompt shows three product descriptions in company style, then asks the AI to write a fourth for a new product.
A legal team provides example contract clause rewrites, teaching the AI their specific simplification approach.
A developer gives three input/output examples to teach the AI an unusual data transformation pattern.