Few-Shot Prompting

Would you like to know in a simple way the opinion of your customers about your restaurant? Using the FewShot technique with tools such as OpenAI, you can automatically determine whether reviews are positive, negative or neutral, beyond a simple star rating. Below, you'll find out how to use this effective technique to rank reviews and drive improvements in your business.

What is the FewShot technique and how does it work?

In artificial intelligence, FewShot is a strategy that helps the model clearly understand what you want by including specific examples in the instructions. Unlike ZeroShot, which is more open-ended and general, FewShot allows you to solve complex but defined problems by providing clear examples that guide the results.

When to use FewShot instead of ZeroShot?

ZeroShot is ideal when you are looking for flexibility and creativity in broad, subjective answers, such as designing a custom vacation plan. FewShot, on the other hand, excels at specific and complex tasks where clear and precise answers are needed, such as correctly classifying reviews into positive, negative or neutral.

How can I classify reviews with FewShot?

To use FewShot in OpenAI and differentiate the feedback you receive, follow these steps:

1. Define clear prompts: tell the model to act as a sentiment analyst, identifying positive, negative or neutral comments based on the opinion expressed.
2. Include practical examples: provide brief, clear examples that demonstrate what you consider positive, negative or neutral.
3. Use a consistent format: use clear labels for floors and ceilings of examples making it easy for the model to identify boundaries.

Here is an example of what your prompt might look like:

Rules:Answer only with the word neutral, positive  or negative.Examples:Comment: "The food was  lousy."Rating: negative.

Comment: "The service was amazing  and the food delicious".Rating: positive.Comment: "The service was good, but the food was very fair".Rating: neutral.

Why are examples important in FewShot?

Each example you include teaches the model to distinguish exactly what you mean, giving a clear and practical guide to identify feelings and avoid ambiguities. Remember that every word and detail greatly influences the outcome, so a good choice of examples positively impacts the final effectiveness of the model.

How many examples should I include in my prompt?

Whether to put many or few examples will depend on the desired performance and the complexity of the problem:

• Each example consumes tokens and will affect the cost of usage.
• The more examples, the more accurate the detailed qualification will be.
• It is recommended to start with a few key examples and add those where you identify previous errors to continuously improve the results.

How can I validate the results of my analysis?

Perform constant testing by adjusting prompt details such as quotation marks or labels and checking if the results vary. Through controlled testing, you can efficiently detect and correct errors, ensuring that the model accurately understands your requirement.

I invite you to comment on what everyday situations you think could benefit from the FewShot technique for sentiment analysis.