Examples of Prompt Engineering, Things to know about Prompt Engineering

Examples of Prompt Engineering.

You can see the basic Examples of Prompt Engineering and analyze it.

What is Prompt Engineering ?

Prompt engineering is a creative discipline that involves creating and optimizing prompts for the efficient use of large language models (LLMs) in a wide range of applications and research areas. Proficiency in prompt engineering is essential for gaining a better understanding of the capabilities and limitations of LLMs. Researchers utilize prompt engineering to enhance the performance of LLMs in various complex tasks, including question answering and arithmetic reasoning. Similarly, developers rely on prompt engineering to design effective and robust prompt-based techniques that interface with LLMs and other tools.

This guide provides an overview of standard prompts, outlining their role in facilitating interaction with LLMs and instructing them to perform specific tasks.

Examples of Prompt Engineering

A good prompt generate and gives you the more accurate and desired results. In simple, we can say the quality of results depend on the accuracy and amount of information you provide. A prompt can be some sort of  instruction or questions along with other details such as inputs or examples.

You can find some basic examples of Prompt Engineering below:

Prompt:

The sky is 

Output:

blue

The sky is blue on a clear day. On a cloudy day, the sky may be gray or white.

In this example, we can see the output given by the language model is confusing and not so accurate. The output might not be the result or the answer we wanted. This happened just because the prompt wasn’t so clear as it was confusing.

We can get the result we wanted by further explaining the prompt. For instance:

Prompt :

Complete the sentence: 

The sky is

Output:

so clear today. 

Well, for the second prompt, the language model gave more specific answer as well asked it to complete the sentence. This approach of designing optimal prompts to instruct the model to perform a task is what’s referred to as prompt engineering.

One more example

We have tried a very simple prompt above. A standard prompt has the following format:

<Question>?

This can be formatted into a QA format, which is standard in a lot of QA dataset, as follows:

Q: <Question>?
A: 

Given the standard format above, one popular and effective technique for prompting is referred to as few-shot prompting where we provide exemplars. Few-shot prompts can be formatted as follows:

<Question>?
<Answer>

<Question>?
<Answer>

<Question>?
<Answer>

<Question>?

And you can already guess that its QA format version would look like this:

Q: <Question>?
A: <Answer>

Q: <Question>?
A: <Answer>

Q: <Question>?
A: <Answer>

Q: <Question>?
A:

Keep in mind that it’s not required to use QA format. The format depends on the task at hand. For instance, you can perform a simple classification task and give exemplars that demonstrate the task as follows:

Prompt:

This is awesome! // Positive
This is bad! // Negative
Wow that movie was rad! // Positive
What a horrible show! //

Output:

Negative

Few-shot prompts enable in-context learning which is the ability of language models to learn tasks given only a few examples.

Examples of Prompt Engineering

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top