GPTPrompts.AI

Few-Shot
Prompting.

Teach ChatGPT patterns with 2-5 examples. Master in-context learning to generate consistent code, writing, classifications, and structured data.

01 — Types

Few-Shot vs Zero-Shot vs One-Shot

TypeExamplesBest For
Zero-Shot0Simple, well-known tasks
One-Shot1Basic format guidance
Few-Shot2-5Complex patterns, styles, edge cases

Few-shot shines when outputs need specific structure (JSON, rhymes, sentiment with reasoning) or when zero-shot fails on domain-specific logic.

03 — Examples

Real-World Few-Shot Examples

Sentiment Analysis

Classify sentiment as positive, negative, or neutral: Text: "This is awesome!" → Positive Text: "This is bad!" → Negative Text: "The movie was okay." → Neutral Text: "Wow that was rad!" → Positive Text: "Service was adequate." → ? Expected Output: Neutral

JSON Extraction

Extract entities as JSON: "Tim Cook leads Apple" → {"person": "Tim Cook", "company": "Apple"} "Elon Musk tweets about Tesla" → {"person": "Elon Musk", "company": "Tesla", "action": "tweets"} "Article mentions Google CEO" → ?

Product Categorization

Classify product type: "iPhone 15 Pro" → Electronics → Smartphone "Nike Air Max shoes" → Apparel → Footwear "USB-C cable 2m" → ? Expected: Electronics → Accessories
04 — Best

Best Practices for Few-Shot Success

  • 2-5 examples max

    More risks context overflow; fewer loses pattern clarity.

  • Diverse examples

    Cover edge cases, variations, common errors.

  • Consistent formatting

    Same delimiters, structure, output style across examples.

  • Quality over quantity

    Realistic, correct examples beat generic ones.

FAQ

Frequently Asked Questions