Few-Shot Prompting
Sometimes the AI needs a little coaching. Few‑shot prompting means giving the model a few examples of what you want before asking the real question. This dramatically improves consistency, especially for tasks with specific formats or unusual requirements.
Few‑shot = provide 2‑5 examples of input‑output pairs, then ask your real question.
Example: Sentiment Classification with Few‑Shot
Review: "I love this product!" → Sentiment: Positive
Review: "It broke after one day." → Sentiment: Negative
Review: "The packaging was nice." → Sentiment: Neutral
Review: "Absolutely terrible, do not buy." → Sentiment:How Many Examples Should You Give?
- 1‑2 examples: Enough for simple patterns (e.g., capital city of a country).
- 3‑5 examples: Good for most tasks (formatting, classification).
- 6+ examples: Rarely needed. If you need many examples, consider fine‑tuning instead.
When to Use Few‑Shot
- You need a very specific output format (JSON, markdown table).
- The task is unusual (e.g., converting slang to formal language).
- Zero‑shot gave inconsistent results.
Keep Examples Short and Relevant
Long examples waste tokens. Your examples should be as short as possible while still showing the pattern. Also, make sure the examples are correct – errors will confuse the model.
Two Minute Drill
- Few‑shot prompting gives 2‑5 examples before the real question.
- Helps the model learn patterns for unusual or format‑sensitive tasks.
- 3‑5 examples are usually enough; more rarely helps.
- Use few‑shot when zero‑shot is inconsistent.
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