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Practice

Zero-Shot Prompting: Asking Without Examples

Let us start with Zero-Shot prompting, the most fundamental of the prompting techniques.

Zero-shot prompting

What Is Zero-Shot Prompting?​

In AI learning, a shot means an example. So zero-shot refers to handling a new task that the model was not specifically trained on, relying solely on what the AI learned during pre-training.

Zero-Shot Prompt Examples​

Zero-Shot Prompt Example 1
Explain the difference between aerobic and anaerobic exercise in 200 characters or fewer.
Zero-Shot Prompt Example 2
Tell me the name of the tallest mountain in the world and its height.

When AI is asked to interpret a prompt and produce a result using only its pre-trained data, with no additional examples, this is called zero-shot prompting.

Most everyday prompts that do not include separate examples are classified as zero-shot prompts.

Note: A prompt that includes a few examples, like the one below, is called a Few-Shot prompt.

Few-Shot Prompt Example
Below are examples of news articles, each classified into a specific category.

Article: "Trade negotiations between countries are making progress. Economic experts view this positively."
Category: Economics

Article: "A famous actor has announced they will appear in a new film. Fans are thrilled."
Category: Entertainment

Now classify the category of the article below.

Article: "According to a recent study, a healthy diet can reduce the risk of heart disease."
Category:

We will cover Few-Shot prompting in detail in the next lesson.

What Are the Advantages of Zero-Shot Prompting?​

  • Fast results: Minimizes the time spent writing prompts and allows you to quickly leverage an existing model without additional training on new domains or tasks.

  • Flexibility: Not limited to specific examples or data, it can flexibly respond to a wide range of tasks across different fields based on the user's prompt.

What Are the Disadvantages?​

  • Accuracy and expertise issues: For tasks in domains the model has not yet learned, answer accuracy may be lower.

  • Time wasted due to ambiguity: When there are no clear guidelines, it can be difficult for AI to provide the right answer. In those cases, more back-and-forth between the user and AI is needed, which can waste time.

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