Zero-Shot Prompting

Sources:

• Prompt Engineering Guide - Zero-Shot

Overview

Modern LLMs, trained on large amounts of data and tuned to follow instructions, are capable of performing tasks zero-shot - without any examples provided.

Example

Prompt:

Classify the text into neutral, negative, or positive.

Text: I think the vacation is okay.
Sentiment:

Output:

neutral

The model correctly classifies sentiment without any prior examples.

When Zero-Shot Works

✅ Works Well	❌ May Struggle
Common tasks (summarization, classification)	Domain-specific terminology
Well-defined output formats	Complex multi-step reasoning
Tasks similar to training data	Novel or unusual formats
Simple instructions	Ambiguous requirements

When to Upgrade

If zero-shot doesn’t work, try:

1. Few-Shot Prompting - Provide 1-5 examples
2. Chain-of-Thought Prompting - Ask for step-by-step reasoning
3. Prompt refinement - More specific instructions

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See Also

• Few-Shot Prompting
• Chain-of-Thought Prompting
• MOC - Artificial Intelligence

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