How To Use New Google Gemini 1.5 Pro (Gemini AI Tutorial) Complete Guide With Tips and Tricks

The video tutorial provides a detailed guide on utilizing Google’s Gemini AI Studio, highlighting the features and capabilities of Gemini 1.5 Flash and Gemini 1.5 Pro models for different use cases. Users are encouraged to experiment with prompts, structured inputs, and tuning options to optimize the model’s performance, along with tips on saving prompts, testing responses, and leveraging the multimodal capabilities of Gemini AI Studio.

Google has officially released the Google AI Studio in various EU and UK countries, offering models such as Gemini 1.5 Flash and Gemini 1.5 Pro. Gemini 1.5 Flash is a fast model with a 1 million token context window, while Gemini 1.5 Pro is more advanced, allowing for long contexts in multimodal use cases. The video tutorial covers different use cases for the 1 million context window, helping users understand the capabilities of these models.

There are three main models discussed: Gemini 1.0 Pro, Gemini 1.5 Pro, and Gemini 1.5 Flash. Gemini 1.5 Pro stands out with its 1 million token context window, enabling users to explore a wide range of tasks within a single context. Instructions on creating prompts for each model are provided, showcasing how to set up system instructions for desired responses. This feature allows users to save time by setting specific response guidelines for the model to follow.

The tutorial also delves into structured prompts within the Gemini AI Studio, where users can input examples of inputs and outputs to customize the model’s responses. By providing specific examples and categorizing inputs and outputs, users can train the model to generate tailored responses. Testing prompts using sample videos and audio capabilities is highlighted, emphasizing the ability to ask questions about video and audio content to receive detailed responses.

Gemini 1.5 Flash is recommended for quick tasks like image or audio identification, while Gemini 1.5 Pro is suitable for detailed descriptions and more complex queries. The process of tuning a model is explained, allowing users to customize the model based on specific data and tasks. This tuning feature enhances the model’s performance and adaptability to user-defined objectives.

In conclusion, the video tutorial provides a comprehensive guide on utilizing the Google Gemini AI Studio, explaining the differences between Gemini 1.5 Flash and Gemini 1.5 Pro models. Users are encouraged to experiment with various prompts, structured inputs, and tuning options to optimize the model’s performance for their specific use cases. Additionally, tips on saving prompts, testing responses, and leveraging the multimodal capabilities of Gemini AI Studio are shared to help users make the most of this advanced tool.