# Few-Shot Prompting Cheat Sheet
_How many examples, chosen how, placed where_
Few-shot prompting is teaching by demonstration. The examples you pick matter far more than how many you use.
> Difficulty: beginner  
> Version: 1.0  
> Updated: 2026-02-23  
> Categories: Prompt Engineering  
> Tags: Chatgpt, Claude, Few Shot

Source: https://invitationbuddy.com/cheat-sheet/few-shot-prompting-cheat-sheet

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## When to use how many
| Shots | Use when | Watch for |
| --- | --- | --- |
| 0 | The task is common and the format is obvious  Drifting format across a batch |
| 1 | The format is unusual but the task is simple  The model copying the example content, not its shape |
| 3–5 | Classification, extraction, house style  Cost — examples are in every request |
| 8+ | Rarely worth it; consider fine-tuning instead  Diminishing returns and context pressure |

## Choosing examples
- [ ] Cover the edge cases, not the easy middle — The average case needs no demonstration
- [ ] Include at least one negative or "none of these" case — Otherwise the model never returns the empty answer
- [ ] Keep every example in the exact output format you want — Any inconsistency reads as permission to vary
- [ ] Balance the classes — Four positives and one negative teaches a prior, not a rule
- [ ] Put the hardest example last — Recency weighting works in your favour

## FAQs
**Do more examples always help?**
No. Returns flatten after about five, and every example is paid for on every request. Beyond that point the money is usually better spent on fine-tuning or on better retrieval.

**Why does the model keep copying my example content?**
Your examples are too similar to each other, so their shared content reads as part of the instruction rather than as incidental. Vary the subject matter and keep only the FORMAT constant.

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_Generated from https://invitationbuddy.com/cheat-sheet/few-shot-prompting-cheat-sheet_