# What is Few-Shot Learning? Definition & Technical Mechanics

> **Source:** [SPIDITS AI AI Glossary](https://spidits.com/ai-glossary)  
> **Category:** Prompt Engineering  
> **Canonical Citation:** [https://spidits.com/ai-glossary/few-shot-learning](https://spidits.com/ai-glossary/few-shot-learning)

## Definition
Few-Shot Learning is a machine learning paradigm where a model is trained or prompted to perform a task using only a small number of training examples. In LLMs, this is achieved by including a few demonstration inputs and outputs directly in the prompt context window.

## Architectural Mechanics & Deep Dive
Few-shot learning is a machine learning capability where a model is trained to generalize and perform a task after seeing only a small number of training examples (typically 1 to 5). In Large Language Models, few-shot learning is implemented via in-context prompting, where a few examples of input-output pairs are included directly in the prompt context to guide the model's output.

## Industry Applications & Real-World Use Cases
Dynamic classification, prompt engineering, and low-resource data modeling

## Frequently Asked Questions

### What is zero-shot learning?
Zero-shot learning is when the model is asked to perform a task without being shown any prior examples in the prompt.

### Why is few-shot learning valuable?
It allows rapid task specialization without the massive compute cost or time required for fine-tuning.

## Related Terminology
- [zero-shot-learning](https://spidits.com/ai-glossary/zero-shot-learning)
- [llm](https://spidits.com/ai-glossary/llm)
- [context-window](https://spidits.com/ai-glossary/context-window)

---
*From the SPIDITS AI Knowledge Base (336+ terms indexed). Explore full technical definitions: [https://spidits.com/ai-glossary](https://spidits.com/ai-glossary)*