Few-Shot Learning
Few-shot learning means giving the model a small number of examples in the prompt so it can mimic the pattern or style for new inputs.
In Simple Terms
Think of it as showing a few sample answers before the real test so the model knows what you want.
Detailed Explanation
You add one to several input-output pairs before the actual question. The model uses them as in-context examples to guide format, tone, or logic. When to use it: when zero-shot is inconsistent or when you need a specific format. Common mistakes: using too many examples and wasting context, or using conflicting examples that confuse the model.
Related Terms
Chain of Thought
Chain of thought is a prompting style where the model is asked to show its reasoning step by step before giving a final answer.
Read morePrompt Engineering
The practice of designing effective inputs to get desired outputs from AI models.
Read moreAI Guardrails
AI guardrails are rules, filters, and checks that keep model inputs and outputs within safe, compliant, and on-brand bounds. They reduce harmful, off-topic, or inappropriate content without retraining the model.
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