techniques

Few-Shot Learning

AI ability to learn new tasks from just a handful of examples.

Definition

Few-shot learning allows models to adapt to new tasks with minimal training examples, typically 2-10 examples.

In-Context Learning: Modern LLMs perform few-shot learning by including examples in the prompt:

``` Translate English to French: cat -> chat dog -> chien house -> ? ```

Advantages: - No fine-tuning required - Quick adaptation - Lower data requirements

Techniques: - Prompt engineering with examples - Meta-learning approaches - Prototype-based methods

Applications: - Rapid prototyping - Low-resource languages - Specialized classifications

Showing an LLM 3 examples of your coding style before asking it to write new code.

The practice of designing effective prompts to get better outputs from AI models.

AI ability to perform tasks it was not explicitly trained on.

LLMs learning to perform tasks from examples provided in the prompt without weight updates.

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