What is Few-Shot Learning?
Providing a few examples in your prompt to teach the AI the pattern you want.
Definition
Few-shot learning is a prompt engineering technique where you include a small number of example input-output pairs in your prompt before the actual task. This teaches the AI the pattern, format, or style you want without any model training. Zero-shot means no examples, one-shot means one example, and few-shot typically means 2-5 examples.
💡 Example
Prompt: "Classify the sentiment: 'Great product!' → Positive. 'Terrible service' → Negative. 'It arrived on time' → Neutral. Now classify: 'The quality exceeded my expectations' →" The AI learns from the examples to respond "Positive."
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What is Few-Shot Learning?
Few-shot learning is a prompt engineering technique where you include a small number of example input-output pairs in your prompt before the actual task. This teaches the AI the pattern, format, or style you want without any model training. Zero-shot means no examples, one-shot means one example, and few-shot typically means 2-5 examples.
How does Few-Shot Learning work in practice?
Prompt: "Classify the sentiment: 'Great product!' → Positive. 'Terrible service' → Negative. 'It arrived on time' → Neutral. Now classify: 'The quality exceeded my expectations' →" The AI learns from the examples to respond "Positive."