ChatGPT Prompt Engineering Tips: Zero, One and Few Shot Prompting

Are you curious to know how  prompt engineering can impact your outputs from a large language model?

If so, you’ll want to read on to learn more about the differences between zero-shot, one-shot and few-shot prompting. 

This blog post will discuss the benefits of each approach, as well as how they can be applied to large language models like ChatGPT, GPT-3, GPT-4 and BLOOM. 

With the right mix of creativity and technological skill, these powerful tools can be combined to create text generation models with impressive accuracy and flexibility for all types of applications.

Read more or watching the YouTube video(Recommended)

YouTube:

What is Prompt Engineering?

Prompt engineering is a process of designing, creating, and testing prompts for natural language generation systems. 

With the right combination of language processing, machine learning and creative writing skills, good prompts can be designed to elicit specific responses from the language model while producing text that is clear, concise, and engaging for human readers.

Going forward, prompt engineering will involve advances in natural language processing and machine learning technology, with potential applications such as chatbots, language translation, summarization, and sentiment analysis.

What is Zero Shot Prompting

Zero-shot prompting enables a model to make predictions about previously unseen data without the need for any additional training. 

This is in contrast to traditional machine learning techniques, which require a large amount of labeled training data to make accurate predictions. 

In the context of prompt engineering, zero-shot learning can be used to generate natural language text without the need for explicit programming or pre-defined templates. 

This can allow for the creation of more diverse and dynamic text generation models, enabling machines to recognize and classify objects without ever having seen any examples of those objects during training. 

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What is One Shot Prompting

One-shot prompting is used to generate natural language text with a limited amount of input data such as a single example or template. 

One-shot prompting can be combined with other natural language processing techniques like dialogue management and context modeling to create more sophisticated and effective text generation systems. 

In the context of prompt engineering, one-shot learning can be used to generate natural language text with a limited amount of input data, such as a single example or template. 

This can allow for the creation of predictable outputs from the large language model.

What is Few Shot Prompting

Few-shot prompting is a technique where the model is given a small number of examples, typically between two and five, in order to quickly adapt to new examples of previously seen objects. 

Few-shot learning can be used in the context of prompt engineering, to create natural language text with a limited amount of input data. Although it requires less data, this technique can allow for the creation of more versatile and adaptive text generation models.

By using advanced techniques such as few-shot prompting, it is possible to create natural language generation models that are more flexible, adaptable, and engaging for human users.

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What is the difference between zero shot, one shot and few shot prompting?

Zero-shot, one-shot and few-shot prompting are techniques that can be used to get better or faster results from a large language model like GPT-3, GPT-4 or ChatGPT.

Zero-shot prompting is where a model makes predictions without any additional training, while one-shot prompting involves a single example or template, and few-shot prompting uses a small amount of data, usually between two and five.

Examples of Zero, One and Few Shot Prompting

Here I have given the examples of Zero, One and Few shot prompting i use in the Youtube video I created about this topic:

Zero Shot Example:

Here i just give the large language model a task to complete without any instructions, the model will then guess what i want in return based on its training and understanding about text:

Write a image description with adjectives and nouns of a Female Cyborg walking in a winter landscape in Norway:

One Shot Example:

Here i give the large language model one example of the output structure i would like to get back, the model will then guess what i want in return based on my example and its training about text:

Write a compressed perfect image description with adjectives and nouns of a Female Cyborg walking in a winter landscape in Norway:

Gorgeous female cyborg, shimmering, sci-fi, armor, strides, snow, trees, banks, frosted, ice, gleaming, metal, blue, optics, robotic, movements, still, pristine, beauty, Nordic, vista –ar 3:2

Write a compressed perfect image description with adjectives and nouns of a Female Cyborg walking in a winter landscape in Norway:

Few Shot Example:

Here i give the large language model 3 examples of the output structure i would like to get back, the model will then guess with much higher accuracy and reliability of what i want in return based on my examples and its training about text:

Write a compressed perfect image description with adjectives and nouns of a Female Cyborg walking in a winter landscape in Norway:

Gorgeous female cyborg, shimmering, sci-fi, armor, strides, snow, trees, banks, frosted, ice, gleaming, metal, blue, optics, robotic, movements, still, pristine, beauty, Nordic, vista –ar 3:2

Write a compressed perfect image description with adjectives and nouns of a Female Cyborg walking in a winter landscape in Norway:

Beautiful female cyborg, powerful sci-fi armor, snow, trees, banks, frosted, ice, gleaming, metal, white, high tech, robotics, still, pristine, majestic, Nordic, epic –ar 3:2

Write a compressed perfect image description with adjectives and nouns of a Female Cyborg walking in a winter landscape in Norway:

Magnificent female cyborg, dazzling futuristic armor, snow-covered, trees, banks, iced, chilly, shining, metallic, electric-blue, lenses, precise, mechanical, motion, serene, picturesque, glorious, Nordic, view. –ar 3:2

Write a compressed perfect image description with adjectives and nouns of a Female Cyborg walking in a winter landscape in Norway:

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Conclusion

Prompt engineering techniques such as zero-shot, one-shot and few-shot prompting can give you more flexibility and control when creating outputs from natural language generation models. 

By taking advantage of the power of these models, you can create more accurate, diverse and engaging outputs that are tailored to the needs of your application.

Ultimately, prompt engineering can help you create the perfect text-outputs for your unique project.

2 Comments

  1. Hi, love the post! Do you think it would be possible to have a copy of your instruction sheet including your custom script and Story prompts? Thanx in advance!

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