Few-Shot Learning

Definitions

1. A technique where a model learns to perform a task from just a handful of examples, either provided in the prompt (in-context learning) or during a brief fine-tuning phase. Contrasts with zero-shot (no examples) and traditional training (thousands of examples).

   In plain English: Teaching an AI to do something by showing it just a few examples instead of millions.

   Example: "Instead of fine-tuning, we use few-shot prompting — three examples of the desired JSON format and the model nails it every time."

Related Terms

• Chain of Thought
• system prompt
• Inference
• Synthetic Data
• Model Card
• Embedding Space
• Federated Learning
• Embedding Model
• Edge AI
• Chain of Thought Prompting
• In-Context Learning
• Autoregressive Model
• Anchor Token
• Prompt Leaking
• Garbage In Garbage Out