Generative AI is the talk of the town and it is likely to be this way for the foreseeable future. However, while general adoption has skyrocketed, the business application of the technology hasn’t been as impressive.

A major roadblock that’s holding businesses back is that off-the-shelf large language models (LLMs) such as the GPT series, are trained on publicly available data until a certain date. This means while they can provide answers to questions like, "What are the best budget vacation spots in Laos?" they can derive nothing from enterprise data.

These cannot even answer the basic questions about the internal HR policies of an organization.

So, why shouldn't the transformative power of generative AI be harnessed for enterprise data, enabling organizations to leverage the full potential of these models within their specific contexts and use cases?

In this in-depth and actionable read, author and Prophecy CEO Raj Bains highlights the three available options for enterprises, their pros and cons, and the takeaways from each option. They are:

Option 1: Build a foundational model

Option 2: Fine-tune a foundational model

Option 3: Prompt engineering

Bains also provides a simple way led by Prophecy for organizations to power generative AI applications using their enterprise data. He also showcases how it can be delivered in a matter of days in a self-service manner, without the need for specialized skills or resources.

Access the article now for FREE to understand which option works best for your use cases. Also, get access to a free 21-day trial of Prophecy’s Data Copilot.