Definition

Few-shot learning is when an AI learns to do something by looking at only a small number of examples. Unlike normal training that needs lots of data, this method teaches the model with just a few hints and still gets good results.

Example

Few-shot learning lets an AI answer questions about a topic after seeing only 2–3 examples.

How It’s Used in AI

Used in chatbots, image classification, and language models like GPT-4. Instead of training on huge datasets, the AI is shown just a few examples in the prompt and still understands the task. It’s useful for fast, flexible learning in real-world applications.

Brief History

Few-shot learning became popular with the rise of large language models like GPT-3 in 2020, which showed strong results even with limited examples.

Key Tools or Models

Models like GPT-3, GPT-4, and Meta's LLaMA support few-shot learning by using prompt-based training rather than traditional retraining.

Pro Tip

Give clear, simple examples that match the style of what you want. The better the prompt, the better the outcome.