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# Few-Shot Prompting: When Examples Help, and When They Quietly Backfire

**[Noor Mohammad](https://daily.dev/sources/ew9hy5zthevyyefagop4g)** · [@noormohammad](https://daily.dev/noormohammad) · 1 min read · 0 upvotes · 0 comments

## Summary

A practical lesson on few-shot prompting pitfalls drawn from a real support ticket classification task. When 3 of 4 examples were bugs, the model learned to default to 'bug' — silently skewing all output. Covers the difference between zero-shot, one-shot, and few-shot prompting, and what makes a good example set: balanced labels, consistent format, correct labels, and inclusion of edge cases. Warns that bad examples don't throw errors — they quietly corrupt results.

## Content

July 2026: classifying support tickets with a clean four-example few-shot prompt. Tidy format, felt bulletproof. Then — almost everything tagged as "bug." Turns out 3 of my 4 examples were bugs, and the model learned "when in doubt, say bug." I didn't teach it the task. I taught it my accident.

That's the few-shot trap: examples resolve ambiguity words can't, but a bad one doesn't throw an error — it silently bends the output. Master AI #6: zero vs one vs few-shot, what a good example set looks like (edge cases, one format, correct labels, balance), and how examples poison results. 🔗
[https://medium.com/@reactjsbd/few-shot-prompting-when-examples-help-and-when-they-quietly-backfire-792bf4a429cb?sharedUserId=reactjsbd](https://medium.com/@reactjsbd/few-shot-prompting-when-examples-help-and-when-they-quietly-backfire-792bf4a429cb?sharedUserId=reactjsbd)

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---

Tags: [#llm](https://daily.dev/tags/llm), [#prompt-engineering](https://daily.dev/tags/prompt-engineering)

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