Which prompt-engineering technique gives the model a few input/output examples so it learns the desired response pattern without any training?
A.Zero-shot prompting
Zero-shot = instruction only, no examples.
B.Few-shot prompting
Correct. In-context examples = few-shot.
C.Model fine-tuning
Fine-tuning changes the model — the opposite of 'no training'.
D.Increasing temperature
Temperature adjusts creativity, not learned patterns.
Why this answer
Few-shot prompting embeds example input→output pairs in the prompt, teaching the model the pattern in-context. Zero-shot gives no examples; fine-tuning actually updates weights (much more work); temperature controls randomness, not pattern learning.
Correct answer: B · Last reviewed 2026-10-26
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