Unlocking the Potential of Structured Prompts: How Gemini Learned Complex Tasks with Just 12 Examples!

Introduction:

In the era of artificial intelligence, large language models like Gemini have captured our imagination with their remarkable capabilities. However, unlocking their full potential often requires carefully crafted prompts. Enter structured prompts — a game-changer in the world of prompt engineering. In this blog post, we embark on a journey with Gemini, exploring how structured prompts enable it to learn complex tasks with just a few training examples.

Background:

I was trying Gemini structured prompts, which I eventually wanted to utilize in one of my side projects. I was thinking of a personalized chatbot with a personal avatar that will automatically respond to the recruiter who approaches the personal real-time replier (I cannot find a better name for now).

I was considering adding a disclaimer that states, "The real-time replier is an AI-based text generator, so it may behave unexpectedly. Please report any errors or inappropriate responses." However, I still plan to include a disclaimer, but I want to express my amazement at Gemini's concise and precise responses, which have broken my doubt. I tested it with complex input prompts, despite the training examples being relatively simple, and I must say that Gemini exceeded my expectations.

Here is an example to do the same:

Let us first have a look at the training data:

You can manually enter this at the 🔗https://makersuite.google.com/app/prompts/new_data

It will look something as below:

Training Data (Just 12 Examples)

You can configure the system to accommodate multiple outputs stemming from a single input, multiple inputs converging to produce the same output, and even scenarios involving multiple inputs generating many outputs.

Responses to Prompts:

Just hitting Gemini with questions out of context

An example of how creatively Gemini is replying

The outputs are generally positive, and the model demonstrates a good understanding of the prompts

A summarized list of responses is here:

Isn’t this cool? I hope you have cool ideas rushing into your mind to utilize this. Below is how you can use it in your project.

Integration with own project:

You also have an option at the top right corner to get code for the same structured prompts.

Once you click on the get code button, you can directly use the given code in your project after creating an API key. The code is offered in multiple languages, as evidenced by the screenshot below.

Rest is for you to explore and create the best out of it! I am a lot excited to see upcoming innovations using such powerful tools.

Conclusion:

Gemini’s journey with structured prompts has illuminated the path towards unlocking the full potential of large language models. Structured prompts serve as a bridge between human intent and machine understanding, enabling models to learn complex tasks with limited data. As we continue to refine and explore the possibilities of structured prompts, we move closer to a future where AI-powered assistants and applications seamlessly integrate into our lives.

Thanks for reading! I hope it was worth your time!