Which general step is involved in applying Generative AI to business problems?

Applying Generative AI to business problems involves developing and utilizing models like large language models (LLMs), which are often engineered to understand and generate human-like text based on input data. The process of creating these models can be quite extensive and usually starts "from scratch," involving not just training data but also fine-tuning to meet specific business needs.

This approach allows businesses to solve a variety of problems by customizing these models to generate meaningful outputs, whether through text, code, or other forms of data relevant to their operations. By starting with foundational LLMs, companies can adapt and innovate solutions tailored to their target audience or specific use cases.

In contrast, while conducting surveys, hiring data scientists, or implementing hardware upgrades may support the overall data infrastructure and model performance, they do not directly capture the essence of harnessing generative AI's capabilities to craft solutions from the ground up. These activities may be ancillary but are not central to the initial step of applying generative AI effectively.