Nov 14, 2024 | AI
Tailoring Large Language Models for Professional-Grade Work
Noah Pruzek, head of Technology Systems, Thomson Reuters, highlights the importance of grounding and human experts in developing professional-grade large language models.
Data curation is crucial for training large language models (LLMs) to operate effectively, especially in professional settings. Generative AI tools like GPT-4 and other mass-market LLMs can get tripped up when it comes to nuanced or specialized tasks, such as navigating the intricacies of U.S. tax codes.
LLMs for professional-grade AI solutions must be tailored with the right mix of data sources and go through a rigorous data architecture process. For enterprise tasks, developers need specialized data plus domain expertise to organize it in such a way that the eventual outputs will be helpful for end-user professionals. Developing a tool for accountants or tax attorneys, for example, involves gathering a wide array of tax codes, regulatory filings, legal interpretations, and more as well as integrating and standardizing this data into a format that LLMs can digest.
As I recently shared in CIO Dive, getting raw data to a place where it can be used to power a generative AI solution requires two steps: grounding and the human factor. Grounding is like giving an LLM a specialized education – analogous to an individual going from an undergrad degree to law school – by augmenting it with use-case-specific information. Human experts, of course, are irreplaceable when it comes to domain expertise, which is essential for creating industry-specific LLMs.
Leading the Technology Services team of engineers at Thomson Reuters is an incredibly rewarding experience. We tackle the unique challenges of creating professional-grade AI solutions that meet the high standards of accuracy and reliability demanded by legal and tax professionals.
Our team is deeply committed to bridging the gap between cutting-edge technology and specialized domain knowledge. We understand that our work doesn’t just involve writing code or developing algorithms; it’s about empowering professionals with tools that enhance their expertise and efficiency. Guiding this team has shown me the impact that thoughtful, well-crafted AI can have in transforming the way professionals work, and it reinforces our dedication to continuous innovation in this space.
Check out my CIO Dive article to learn more about the need for data curation and data stewardship in developing professional-grade AI solutions.
This is a guest post from Noah Pruzek, head of Technology Systems, Thomson Reuters.
