Abstract
In today's fast-paced world, the rapid introduction of new product features necessitates accurate and timely documentation. Traditional manual documentation methods are inefficient, error-prone, and often result in outdated information. Techniques are presented herein to address these challenges by leveraging Large Language Models (LLMs) to automate the generation of precise and reliable documents by associating data with well-defined role-based source information. The impact of this solution goes beyond generating documentation for new features; it can also be extended to enable targeted stakeholder interactions, to identify speakers in recorded conversations, and to classify unclassified dialogues.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Mukund, Laxmi and S K, Shraddha, "PROVIDING ENHANCED LEARNING IN LLMS BY ASSOCIATING DATA WITH ROLE-BASED SOURCE INFORMATION", Technical Disclosure Commons, (December 23, 2024)
https://www.tdcommons.org/dpubs_series/7682