Few-shot prompting is an AI technique that enables models to learn and perform tasks with minimal exposure to new examples. It stands apart from zero-shot learning (no example) and one-shot learning (single example), as it uses a small number of examples for learning. Prompts play a pivotal role in guiding the model to provide desired outputs.
How to Use the Few-Shot Prompting Technique
To effectively use few-shot prompting, follow these steps:
Identify the task: Clearly define the task you want the model to perform, such as text summary or image reasoning.
Write the prompts that effectively guide the model towards the desired output.
Provide “few” examples: Provide more than two examples for the model to learn from. The optimal number of examples may vary depending on the task and the specific model. Generally speaking, the more example and data you provide to the language model, the better the model can provide the desired output.
Fine-tune the prompts: Iterate and refine the prompts and examples until the model provides satisfactory results.
References:
https://promptsninja.com/featured/what-is-few-shot-prompting-with-chatgpt-a-beginners-guide/#:~:text=The%20process%20of%20few%2Dshot,and%20large%2Dscale%20training%20data.
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