The key difference between one-shot prompting and few-shot prompting lies in the number of examples provided within the prompt and the level of guidance offered to the Large Language Model (LLM).


One-Shot Prompting:

  • Number of Examples: Provides a single example within the prompt.
  • Guidance Level: Offers a basic level of guidance by showcasing the format, content, and relationship between input and output for the desired task.
  • Benefits:
    • Simpler and faster to implement compared to few-shot prompting.
    • Useful for tasks where the format is crucial or the task is relatively simple.
  • Limitations:
    • Limited guidance might not be sufficient for complex tasks or tasks with nuanced requirements.
    • Heavily reliant on the quality and relevance of the single example used.
    • The LLM might simply mimic the provided example without truly understanding the underlying concept.

Few-Shot Prompting:

  • Number of Examples: Provides multiple examples (typically 2-shot, 3-shot, 5-shot) within the prompt.
  • Guidance Level: Offers a more comprehensive level of guidance by demonstrating multiple instances of the desired task outcome. This allows the LLM to identify patterns and generalize the underlying principles.
  • Benefits:
    • Improved performance on complex tasks compared to one-shot prompting.
    • More flexibility as the technique can be adapted to various tasks by adjusting the format and content of the examples.
    • Still a relatively lightweight approach compared to fine-tuning.
  • Limitations:
    • Effectiveness depends on the complexity of the task. For highly complex reasoning problems, even a few examples may not be sufficient.
    • Quality and relevance of the provided examples significantly impact the LLM's learning and performance.
    • The LLM might learn to mimic the provided examples without truly understanding the underlying concepts.

Choosing Between One-Shot and Few-Shot:

  • Use one-shot prompting when the task is simple, you need a quick response, or you only have one relevant example.
  • Use few-shot prompting when the task is more complex, you need more control over the output, or you have access to multiple relevant examples.

In essence, one-shot prompting is like giving the LLM a single instruction, while few-shot prompting is like providing a mini-tutorial with