What is zero shot, single shot and few shot prompting

 Zero-shot, one-shot, and few-shot prompting refer to different techniques used to guide language models (like GPT) to perform tasks by providing varying amounts of examples in the prompt. These techniques are critical in natural language processing (NLP) as they dictate how much context or task-related information is provided to the model. Here's a breakdown of each:

1. Zero-shot Prompting

In zero-shot prompting, the model is asked to perform a task without being given any example in the prompt. The model is expected to understand and generate a response based solely on the task description.

Example:

Task: Classify the sentiment of a sentence.

Prompt:

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Classify the sentiment of this sentence: "I love this product."

The model is directly asked to classify sentiment without any prior examples.

Useful when the model has already been pre-trained on similar tasks and can infer the task from context.

2. One-shot Prompting

In one-shot prompting, you provide the model with one example of how the task should be performed. This single example serves as a guide for the model to understand the expected format of the response.

Example:

Task: Classify the sentiment of a sentence.

Prompt:

kotlin

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Classify the sentiment of this sentence: "I hate this service." Answer: Negative.

Now classify the sentiment of this sentence: "I love this product."

The model is provided with one example (I hate this service classified as Negative) to understand the task before being asked to classify a new sentence.

3. Few-shot Prompting

In few-shot prompting, you provide the model with a few examples (typically 2-5) to guide it in understanding how to perform the task. These examples help the model generate responses that match the desired output pattern.

Example:

Task: Classify the sentiment of a sentence.

Prompt:

mathematica

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Classify the sentiment of these sentences:

1. "I hate this service." Answer: Negative.

2. "This is the worst experience ever." Answer: Negative.

3. "I love this product." Answer: Positive.

Now classify the sentiment of this sentence: "The food was amazing."

The model is given three examples, showing both positive and negative classifications, before being asked to classify the final sentence.

When to Use Each Technique

Zero-shot: Ideal when you want the model to generalize based on prior training without providing specific examples (suitable for simple tasks where the model has context).

One-shot: Useful when the task may not be as straightforward, but a single example is enough for the model to catch on.

Few-shot: Best for more complex tasks where the model needs multiple examples to understand the nuances of how the task should be performed.

references:

OpenAI