Thinking about implementing generative AI (GenAI) capabilities in your organization? Before you get started, examine the datasets you’ll be using to train your models and be sure they have the quality, features, security precautions and scalability you need to optimize your AI outcomes.
Since the launch of OpenAI’s generative AI (GenAI) tool ChatGPT in late 2022, organizations of all kinds have started exploring ways to incorporate its capabilities into their products, services and daily operations. Today, a rapidly expanding variety of GenAI tools enable companies to automatically write copy for websites and marketing, create images and videos, generate software code, analyse data, conduct research and much more.
Unlike traditional AI applications, GenAI tools aren’t trained using specific data for specific tasks but are built on foundation models using a vast amount of varied data – not just words, but images, video, audio and other types of information. These large bodies of training data enable the GenAI tools they power to generate accurate, intelligent-sounding responses to almost any prompt… or to occasionally “hallucinate” answers full of falsehoods.
To maximize the odds of the first outcome and minimize the chances of the second, it’s important to build your foundation model on high-quality data and best practices.