Few-shot learning is a game-changer in prompt engineering. By providing just a few examples, you can dramatically improve AI performance on specific tasks. Learn this powerful technique.
Understanding Few-Shot Learning
Instead of explaining what you want, you show the AI what you want through examples. This approach mimics how humans learn—through observation and imitation.
How Few-Shot Works
Your prompt includes:
- Clear instruction of the task
- 2-5 examples showing ideal input-output pairs
- The new query you want answered
Example: Classification Task
Instruction: “Classify the following statements as positive or negative sentiment.”
Examples:
“I love this product!” – Positive
“This is awful and broken.” – Negative
“It’s okay, nothing special.” – Neutral
Your Query: “The food was delicious and well-prepared.” – ?
Best Practices
- Include 2-5 diverse, representative examples
- Match examples to the complexity of your query
- Use consistent formatting
- Include edge cases in examples when possible
- Keep examples similar in length to your actual task
Keywords: few-shot learning, prompt examples, in-context learning, AI training