Few-shot vs zero-shot — when do you use each?

easy

Answer

$Zero-shot: task description only.$
$Best for well-known task formats or when tokens matter (long few-shot examples eat context).$
$Few-shot: 1-5 example pairs.$
$Best when: (1) output format is non-obvious and needs demonstration; (2) task is unfamiliar; (3) you want deterministic style.$
$Modern instruction-tuned LLMs are strong zero-shot for common tasks, but few-shot still gives 2-10% gains on niche tasks.$
$Rule: start zero-shot, add examples only if quality misses.$

Check yourself — multiple choice

Zero-shot is always better
Zero-shot for common tasks / short prompts; few-shot when output format needs demonstration or on niche tasks (+2-10%)
Few-shot always wins
Same effectiveness

Zero-shot for familiar tasks; few-shot for format demonstration or niche domains.

#prompting#in-context-learning

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