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Few-Shot Prompting: Refine your generative AI results

- Reading Time: 2 minutes
Few-Shot Prompting: Refine your generative AI results

With the democratisation of artificial intelligence tools, users are discovering infinite possibilities. Provided they know how to talk to AI. And to do that, there are several prompt engineering techniques. One of the most popular is Few-Shot Prompting. Find out more about this method.

What is Few-Shot Prompting?

Few-Shot Prompting is a prompt engineering technique that involves showing the AI a few examples (or shots) of the desired results. Using the examples provided, the model learns a specific behaviour, enabling it to carry out similar tasks.

For example, a marketing manager could use this Prompting technique to classify customer comments as positive or negative. To do this, he presents the AI with several comments, specifying whether they are positive or negative.

Using these examples, the language model should be able to identify the patterns associated with positive and negative feelings. It will then be able to classify the other comments.

Why use Few-Shot Prompting?

Since ChatGPT was democratised, some users have seen it as a revolutionary tool, while others still doubt its capabilities.

The difference between the two? Mastery of prompts. And in particular its techniques such as few shot Prompting.

Indeed, large language models (LLMs) like ChatGPT are trained with massive volumes of data. But all this information is supposed to meet the needs of as many people as possible.

However, if you ask it to carry out a specific task, there is a high risk that it will provide you with a very general response, far removed from your initial expectations.

Few-Shot Prompting is essential for refining artificial intelligence responses. In this sense, it’s a formidable fine-tuning strategy.

How to use Prompting in Few-Shot?

To obtain truly relevant results with Few-Shot Prompting, you need to apply the right methods. Here are a few tips:

  • Structure your prompt properly: the examples proposed always consist of two values: the value to be analysed and the desired result.
  • Provide the right format: the generative AI tool will present you with output data that is identical to the data you provide as input.
  • So your shots must indicate the exact format you want. For example, “surname; first name; profession; age”.
  • Provide a sufficient number of examples: in general, it is advisable to provide the AI with between 3 and 5 examples.

Discover prompt engineering with DataScientest

As artificial intelligence tools continue to develop, so do prompt engineering techniques. And not just Few-Shot Prompting. Other methods are appearing regularly. To find out which techniques are the most effective, it’s vital to get trained.

Fortunately, at DataScientest, we have created a course dedicated to prompt engineering. By the end of the programme, you’ll know how to communicate effectively with the major language models, obtaining all the results you want.

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