How to choose the right gen AI models + 185 real world use cases

This edition, we bring you another round of perspectives from Warren Barkley , Vertex AI product leader, as he explores some of the distinctions and trade-offs between various gen AI models. They’re often different and suited to specific tasks, so having a deeper understanding of where they work best is crucial for their success. And then what can you do with those models? Well, check out our updated list of 185 gen AI use cases and agents for inspiration from global businesses and organizations putting AI into production right now.

Gen AI models, while incredibly flexible and versatile, are not a catch-all solution for every challenge — nor is a single model capable of solving all of your problems . Some gen AI models are better suited to certain tasks while others may be a better choice depending on your industry and other requirements you have around performance, privacy, complexity, and cost.

And with so many model types and sizes out there, how do you make sense of them all and understand how to use them?

In recent months, questions about model choice have come up again and again in my conversations about adopting and scaling gen AI. Many leaders want more clarification on what different gen AI models can actually do and guidance about how to use them to support their strategic objectives.

Understanding the nuances of different types of gen AI models and how they relate to the broader context of your applications is therefore a critical step towards using gen AI to drive business value and innovation. In this edition, we’ll explore some of the key points we often highlight that have helped our own customers navigate this topic successfully.

Considering different gen AI models

The gen AI model landscape is wide-ranging and diverse, from foundation models to domain- or task-specific large language models to smaller, single-purpose micro models. In general, when asking about gen AI models, many organizations may be referring to foundation models — large-scale AI models that have been pre-trained on massive datasets to perform a wide variety of tasks, including content generation, data augmentation, creative problem-solving, and many more.

For example, Google’s multimodal foundation model Gemini can generalize and understand, operate across, and combine different types of information, such as text, audio, image, videos, and code. Foundation models can be adapted and fine-tuned for a broad range of applications, providing a strong starting point for many types of business use cases.

With the rise of enterprise adoption of gen AI, there is an increasing need for models that can generate output, tailored for particular industries, fields, and types of tasks. Unlike general-purpose foundation models, domain-specific models are trained to interpret context, terminology, and even jargon in a particular area, such as healthcare or cybersecurity.

Similarly, task-specific models are built to perform a specific task or a set of closely related tasks, such as translation, code completion, and image or video generation. While these models can handle more specialized tasks with greater accuracy and relevancy, they offer lower adaptability and often carry higher development costs.

Continue reading on Transform with Google Cloud.

185 real-world gen AI use cases

As part of our recent Gemini at Work event , we updated our ongoing list of generative AI use cases, which now number 185 distinct AI applications and agents from some of the world’s leading companies, governments, and organizations.?

Not only do they demonstrate that AI is truly finding meaningful real-world applications, but also we’re starting to see emerging trends in where organizations are making the best use of AI.