Few Shot Prompting

If you add only a few examples to your prompt, the model will be able to perform the task with only a few examples. This is called few-shot prompting.

Examples of Few-Shot Prompting

English: Dog → Spanish: Perro
English: Cat → Spanish: Gato
English: Hello → Spanish: ?

5 + 10 = 15
7 * 6 = 42
9 / 3 = ?

10 miles to km = 16.09 km
5 feet to meters = 1.524 meters
20 pounds to kg = ?

2023-08-17 → August 17, 2023
2021-12-01 → December 01, 2021
2023-05-15 → ?

Your task is to answer in a consistent style:
Son: Dad, what is motivation?
Dad: Motivation is when you get something you want.
Son: Dad, what is inspiration?

Tips for Crafting Effective Few-Shot Prompts

Be clear and concise: Make sure your examples are easy for the model to understand.
Uniform Format: Ensure all your examples follow the same format. This helps the model to learn the pattern.
Quality over Quantity: Few-shot doesn’t mean many examples; a few well-crafted examples can be more effective.
Experiment: If the output isn’t as expected, try refining your examples or changing them slightly.

Applications of Few-Shot Prompting

Language Translation: Use few-shot prompting to train a model for multilingual translations.
Data Conversion: Train a model to convert units, date formats, and more.
Content Generation: Use few-shot prompting to generate creative content, like writing, art, and music.

Challenges and Considerations

Overfitting: Too specific examples can make the model learn the examples by heart, and it may not generalize well.
Ambiguity: Make sure your examples are clear and leave little room for interpretation to avoid unexpected results.