Applications
Few-Shot Learning
A prompting or training approach where a model is shown a small number of examples before handling a new task.
Few-shot learning means giving a model a handful of examples so it can infer the pattern and apply it to a new input. In prompt engineering, this usually means including 2 to 5 demonstration pairs before the real task.
For example, if you want an LLM to label support tickets by urgency, you can provide a few labeled examples in the prompt. The model then imitates the pattern for the unseen ticket at the end.
Why it works: powerful language models can infer task structure directly from examples inside the prompt, without needing a full fine-tune.
Benefits of Few-Shot Prompts
- Improved formatting — the model follows demonstrated structure
- Better task understanding — examples clarify expectations
- Fast iteration — no retraining required
- Useful for niche tasks — especially when instructions alone are ambiguous
Few-shot prompting is closely related to in-context learning. It is one of the most practical and widely used techniques for improving output quality in real AI applications.