Abstract
This disclosure describes a framework to automate complex decisions by combining the strengths of human-defined logic with insights derived from large language models (LLMs). Human decision-makers define templates, known as ‘decision models’ or ‘decision schema,’ for the structure and the core logic of the decision-making process. The templates are augmented or populated using output derived from LLM analysis. The LLM can be used to provide insights, generate text, or fill in specific parts of a pre-defined template, thereby enhancing the human-designed logic. The framework uses the completed, LLM-enhanced templates to automate decisions efficiently and transparently. Effectively, LLMs augment and structure human expertise in decision-making via a template-driven approach. The framework accelerates development cycles and enables organizations to navigate the complexities of dynamic business environments with agility. The framework can harness the power of LLMs while retaining human oversight, resulting in the creation of sophisticated, nuanced decision logic that can be readily customized and adapted to diverse use cases.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
S, Gunalan and B, Akshatha, "Adaptive Decision Engine Using Large Language Models and Configurable Schema", Technical Disclosure Commons, (June 02, 2025)
https://www.tdcommons.org/dpubs_series/8182