Abstract
Artificial intelligence (AI) models that depend on context from external data sources may produce outdated responses as underlying information, such as database schemas or web content, changes over time. The described system compartmentalizes AI model context into static and dynamic components. A static context may comprise fixed rules, safety policies, and format requirements. A dynamic context can be populated by a user-provided script configured to run at a specified frequency. This script can fetch, structure, and cache data from external sources, for instance APIs or databases, using stored credentials. This method allows AI models to operate with more current information from external environments, which can help outputs remain synchronized and adhere to predefined operational policies, potentially reducing the need for manual context updates.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Hawker, Colby and Arora, Piyush, "A System for Synchronizing Artificial Intelligence Model Context with External Data Sources", Technical Disclosure Commons, (September 22, 2025)
https://www.tdcommons.org/dpubs_series/8609