Abstract
Auditing the quality of complex enterprise documents can present challenges, as manual reviews may be unscalable and subjective, while some automated tools may lack the ability to evaluate content against specific business objectives. Systems and methods are described that can programmatically parse a document's content and layout within a user's digital workspace. This extracted information can be synthesized with user-supplied external context, such as project goals or key performance indicators. A computational model, for example, a generative artificial intelligence model, can then evaluate the combined, contextualized data against a configurable, rubric-based framework. This approach may provide automated, context-aware document quality auditing, delivering quantitative scores and actionable recommendations directly within an editing environment to facilitate document revision and alignment with strategic goals.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Hassan, Zeeshan and S K, Ganesh, "Context-Aware Document Quality Auditing Using Generative AI", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12718