Few-Shot Prompting

Few-shot prompting is an advanced technique where the AI is provided with a small number of examples (typically 1-5) within the prompt to guide its response. These examples demonstrate the desired format, style, or reasoning process, helping the model better understand the user's expectations and adapt to specific requirements or domains.

Few-shot prompting bridges the gap between zero-shot (no examples) and many-shot (large training datasets) approaches. By showing the model a handful of representative input-output pairs, users can teach the AI new formats, clarify ambiguous instructions, or improve performance on specialized or nuanced tasks.

Key Characteristics

• Includes 1-5 examples in the prompt, chosen to illustrate the desired behavior
• Demonstrates the expected input-output relationship, format, or style
• Improves performance on less common, complex, or domain-specific tasks
• Reduces ambiguity by showing the model what is expected
• Can be used to teach the model new formats, terminology, or reasoning patterns
• Allows for rapid adaptation to new domains or requirements without retraining

When to Use

• When the task is ambiguous or requires a specific style, structure, or tone
• For custom formats, domain-specific outputs, or specialized terminology
• When zero-shot performance is insufficient or inconsistent
• For tasks where consistency, accuracy, or adherence to a template is important
• When you want to quickly prototype or experiment with new prompt designs

Strengths and Limitations

• Strengths:
  • Increases accuracy and reliability for specialized, nuanced, or unfamiliar tasks
  • Allows for customization and adaptation to new domains or requirements
  • Can teach the model new formats, styles, or reasoning processes on the fly
  • Reduces the need for extensive retraining or fine-tuning
• Limitations:
  • Prompt length is limited by the model's context window; too many or lengthy examples can crowd out the actual task
  • Poorly chosen or inconsistent examples can confuse the model or degrade performance
  • May require experimentation to find the optimal number and type of examples
  • Not as effective for tasks that require large-scale learning or generalization

Example Prompt

• "Translate the following sentences to French:

1. The cat is sleeping. -> Le chat dort.
2. I like apples. -> J'aime les pommes.
3. The book is on the table. ->"

• "Convert the following dates to ISO format:

1. March 5, 2025 -> 2025-03-05
2. July 20, 2024 -> 2024-07-20
3. January 1, 2023 ->"

Example Result

Le livre est sur la table.
2023-01-01

Best Practices

• Choose representative, high-quality examples that clearly illustrate the desired behavior
• Keep examples concise, relevant, and consistent in format
• Place the most important or typical examples first, as the model may pay more attention to earlier examples
• Use consistent formatting, language, and structure for clarity
• Test and iterate to find the optimal number and type of examples for your task
• Avoid overloading the prompt with too many or overly complex examples
• Review outputs to ensure the model is following the intended pattern