One-shot and Few-shot Prompting are prompting techniques within the larger context of Prompt Engineering that involve providing demonstrations or examples to Large Language Models (LLMs) to guide their output. These techniques are employed when the most basic form of prompting, zero-shot prompting (which provides a task description without any examples), does not yield the desired results.
In the broader field of Prompt Engineering, which is the iterative process of crafting effective text prompts to guide LLMs to produce specific outputs, one-shot and few-shot prompting represent a step up in complexity and guidance compared to zero-shot prompting. Prompt engineering considers various aspects of a prompt, including word choice, style, tone, structure, and context, as these significantly affect the prompt’s efficacy.
Here’s a more detailed breakdown of One-shot and Few-shot Prompting within this context:
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Relationship to Zero-shot Prompting: The sources indicate that zero-shot prompting is the simplest type of prompt, relying solely on a task description. When this general approach fails to produce satisfactory results, providing examples through one-shot or few-shot prompting becomes the next logical step.
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One-shot Prompting: This technique involves providing the LLM with a single example of the desired input-output format or task completion. The idea is that this single demonstration can help the model understand what is being asked and imitate the provided example to complete the task effectively.
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Few-shot Prompting: This technique goes further by supplying the LLM with multiple examples. This shows the model a pattern that it needs to follow, increasing the likelihood that the model will adhere to the desired output structure, style, or format. Providing examples is highlighted as the “most important best practice” in prompt engineering and is described as a “powerful teaching tool”.
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Number of Examples: The number of examples required for effective few-shot prompting can vary depending on factors such as the complexity of the task, the quality of the examples, and the capabilities of the LLM. However, a general rule of thumb is to use at least three to five examples. More complex tasks might necessitate more examples, while models with input length limitations might require fewer. A good starting point for testing accuracy in few-shot prompting is around six examples.
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Quality and Relevance of Examples: When selecting examples for one-shot or few-shot prompts, it is crucial to use examples that are relevant to the task, diverse, of high quality, and well written. Even a small mistake in an example can confuse the model and lead to undesired outputs. Including edge cases in the examples can also be important if the goal is to generate output that is robust to a variety of inputs.
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In-context Learning: As highlighted in one of the transcripts, providing examples in few-shot prompts leverages the LLM’s in-context learning abilities, allowing it to learn the desired pattern directly from the context of the prompt and continue that pattern.
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Use Cases: The sources illustrate the use of few-shot prompting for tasks like translation by providing examples of word pairs and vocabulary extraction by demonstrating the desired output format. These examples show how providing a few instances of the task can significantly improve the model’s ability to perform it correctly.
In summary, One-shot and Few-shot Prompting are crucial techniques within Prompt Engineering that build upon the foundation of Zero-shot Prompting. By providing carefully selected examples, prompt engineers can significantly enhance the performance of LLMs, especially for tasks where clear patterns or specific output formats are required. This method of “teaching by example” is considered a fundamental best practice in guiding LLMs effectively.