What category does Few-Shot Learning belong to?

Few-Shot Learning belongs to the "Techniques" category in personal knowledge management and productivity.

What are the key topics related to Few-Shot Learning?

Key topics related to Few-Shot Learning include: ai, machine-learning, prompting, techniques, learning.

Few-Shot Learning

Q: What are alternative names for Few-Shot Learning?

Few-Shot Learning is also known as: Few-shot prompting, In-context learning, Example-based prompting.

Training or prompting AI with just a few examples to perform new tasks.

Also known as: Few-shot prompting, In-context learning, Example-based prompting

Category: Techniques

Tags: ai, machine-learning, prompting, techniques, learning

Explanation

Few-shot learning is the ability to learn new tasks from only a few examples - a capability that distinguishes modern large language models. Traditional machine learning requires thousands of examples; few-shot learning achieves useful performance with 2-10 examples in the prompt. How it works with LLMs: provide a few input-output examples in your prompt, then give a new input - the model learns the pattern and applies it. Example format: 'Text: [example1] -> Category: [label1], Text: [example2] -> Category: [label2], Text: [new input] -> Category:' Why it matters: enables rapid task adaptation without retraining, makes AI accessible to non-ML experts, and allows customization through examples rather than code. Best practices: choose diverse, representative examples; be consistent in formatting; start with 2-3 examples and add more if needed; and order examples thoughtfully. Variations: one-shot (single example), zero-shot (no examples, just instructions), and many-shot (more examples for complex tasks). For knowledge workers, few-shot learning enables: quickly adapting AI to specific tasks, showing rather than explaining what you want, and creating consistent outputs for repetitive tasks.

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