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Few-shot Prompting Examples

January 21, 2024 | AI

In the ever-evolving world of artificial intelligence and machine learning, the concept of ‘few-shot prompting’ has emerged as a groundbreaking technique, reshaping how we interact with and leverage the capabilities of language models. At its core, few-shot prompting involves providing a language model, like GPT-4, with a minimal set of examples – typically between two to five – to guide its understanding and generate responses tailored to specific tasks. This approach marks a significant shift from traditional methods that require extensive training datasets. In this article, we delve into the nuances of few-shot prompting, exploring its definition, discussing its key aspects, and presenting practical examples that demonstrate its remarkable versatility and efficacy.

What is few-shot prompting?

Few-shot prompting is a technique used in natural language processing (NLP) where a language model, like GPT-4, is provided with a small number of examples (usually between 2 to 5) to guide its understanding and responses for a specific task. This method is particularly useful when dealing with tasks that require a specific format, style, or nuanced understanding.

10 key aspects of few-shot prompting

1. Contextual Guidance

Few-shot prompts provide the model with context or examples of what is expected in the response. This guidance can be crucial for tasks that are not common or are highly specific.

2. Enhanced Accuracy

By showing the model exactly what kind of response is desired, few-shot prompting can significantly improve the accuracy and relevance of the model’s output, especially in tasks that might be ambiguous or complex.

3. Learning from Examples

Language models like GPT-4 learn patterns from the examples given in the prompt. These examples act as a template for the model, helping it understand not only the content but also the structure and style of the desired response.

4. Versatility

Few-shot prompting is versatile and can be applied to a wide range of tasks, including text summarization, translation, data extraction, creative writing, question answering, and much more.

5. Reduction in Training Data

Traditional machine learning models require vast amounts of training data. Few-shot prompting, however, allows models to perform tasks effectively with much less data, making it a more practical approach in many scenarios.

6. Prompt Design

The effectiveness of few-shot prompting depends significantly on the design of the prompt. It requires careful selection of examples that are representative of the task and clear in illustrating the desired output format.

7. Limitations

While few-shot prompting can be powerful, it has limitations. The quality of output depends heavily on the relevance and clarity of the examples provided. If the examples are too vague or not aligned with the task, the model’s responses may be inaccurate or off-topic.

8. Adaptability

Few-shot prompting is particularly useful in situations where a model needs to adapt to new tasks or formats quickly without the need for extensive retraining or fine-tuning.

9. Human-like Learning

This approach mimics how humans often learn – by seeing a few examples and then applying that understanding to similar tasks. It’s an attempt to make AI models more adaptable and intuitive in their learning processes.

10. Interactivity

Few-shot prompting can also be interactive. For instance, a user can provide feedback on the model’s responses, and the model can use this feedback, along with the initial examples, to refine its understanding and outputs.
In essence, few-shot prompting is a way of ‘teaching’ language models to perform specific tasks or understand specific contexts with just a few examples, making them more flexible and efficient in handling a wide range of queries.

Few-Shot Prompting Examples

In each of the examples below, we’ve given the model a specific task with a few examples to demonstrate the desired format or type of response. This helps the model to understand and replicate the pattern in its output.

1. Text Summarization


Summarize the following paragraphs in one sentence each:
Paragraph 1: "The history of ancient Rome is marked by many significant events. One of the most notable is the reign of Julius Caesar, who expanded the Roman Republic's territories significantly before his assassination."
Paragraph 2: "Photosynthesis is a process used by plants and other organisms to convert light energy into chemical energy that can later be released to fuel the organisms' activities."

Expected Response:

Paragraph 1 Summary: Julius Caesar's reign, marked by territorial expansion, is a key event in ancient Rome's history, ending with his assassination.
Paragraph 2 Summary: Photosynthesis allows plants and organisms to convert light into chemical energy for their activities.

2. Language Translation


Translate the following sentences from English to French:
Sentence 1: "Welcome to our home."
Sentence 2: "What time is the meeting scheduled to start?"

Expected Response:

Sentence 1 Translation: Bienvenue chez nous.
Sentence 2 Translation: À quelle heure est prévue la réunion ?

3. Data Extraction


Extract the main information from the following business cards:
Card 1: "John Doe, CEO of Innotech, Email:, Phone: 555-1234"
Card 2: "Jane Smith, Marketing Director, Bright Solutions, Contact:, Tel: 555-5678"

Expected Response:

Card 1: Name: John Doe, Position: CEO, Company: Innotech, Email:, Phone: 555-1234
Card 2: Name: Jane Smith, Position: Marketing Director, Company: Bright Solutions, Email:, Phone: 555-5678

4. Creative Writing (First Lines)


Provide the first line of a story based on the following themes:
Theme 1: A mysterious island.
Theme 2: A journey through space.

Expected Response:

Theme 1: As the fog cleared, the silhouette of an uncharted island emerged, veiled in mystery and ancient lore.
Theme 2: Staring out of the spacecraft window, I marveled at the star-studded void, embarking on a journey beyond the edges of the known universe.


Few-shot prompting stands as a testament to the remarkable strides made in the field of natural language processing. By enabling language models to adapt quickly to a wide range of tasks with minimal examples, this technique not only enhances the efficiency and accuracy of AI responses but also opens up new possibilities for AI applications in various domains. The examples presented in this article highlight the practicality and adaptability of few-shot prompting, showcasing its potential to transform complex tasks into manageable ones. As this technology continues to evolve, it promises to unlock even more innovative ways of harnessing the power of AI, making it an indispensable tool in the ever-expanding digital landscape.