How to Train Generative AI Using Your Company’s Data
hbr.org
Morgan Stanley, for example, used prompt tuning to train OpenAI’s GPT-4 model using a carefully curated set of 100,000 documents with important investing, general business, and investment process knowledge. The goal was to provide the company’s financial advisors with accurate and easily accessible
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Summary
Generative AI models like ChatGPT have impressive language skills but lack knowledge of proprietary content. Companies can leverage generative AI to manage and disseminate their own knowledge assets, improving performance and innovation. There are three primary approaches to training generative AI models with proprietary content: training from scratch (requires massive data and resources), fine-tuning existing models (less data and resources required), and prompt-tuning (using prompts to customize existing models). Content curation, quality assurance, legal and governance issues, and shaping user behavior are important considerations. While challenges exist, the benefits of generative AI for knowledge management make it worth the effort.
Top Highlights
- Morgan Stanley, for example, used prompt tuning to train OpenAI’s GPT-4 model using a carefully curated set of 100,000 documents with important investing, general business, and investment process knowledge. The goal was to provide the company’s financial advisors with accurate and easily accessible knowledge on key issues they encounter in their ro...
- Many companies are experimenting with ChatGPT and other large language or image models. They have generally found them to be astounding in terms of their ability to express complex ideas in articulate language. However, most users realize that these systems are primarily trained on internet-based information and can’t respond to prompts or question...
- Leveraging a company’s proprietary knowledge is critical to its ability to compete and innovate, especially in today’s volatile environment.
- These objectives were also present during the heyday of the “knowledge management” movement in the 1990s and early 2000s, but most companies found the technology of the time inadequate for the task. Today, however, generative AI is rekindling the possibility of capturing and disseminating important knowledge throughout an organization and beyond it...
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