Abstract
A challenge may arise for automated information retrieval systems, such as generative artificial intelligence applications, when parsing webpages designed primarily for human visual consumption, potentially leading to incomplete or inaccurate responses to user queries. To address this, systems and methods can employ a multi-agent framework to optimize webpage content. The system can parse a webpage, generate a set of potential user queries, and identify informational gaps by determining which queries are not answered by the page's existing content. A business representative can then provide verified answers for these gaps, which an agent can programmatically embed into the webpage's source code in a machine-parsable format. This iterative process may improve the likelihood that automated systems can accurately retrieve comprehensive information from the webpage, often without substantially altering the visual design for human visitors.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Melvix, Lenord, "An Agent-Based System for Optimizing Webpage Content for Automated Information Retrieval", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11010