Abstract
Large language models are capable of automatically generating functional software code in response to user prompts. However, in current practice, LLM-generated code typically does not include comments and/or supporting information that links specific parts of the generated code to corresponding practices and/or intent of the end users. This disclosure describes a comprehensive, extensible repository of standard codes that can be used to annotate no-code or low-code software solutions where the application functionality is implemented using LLM-generated software code. The standard codes can be used to obtain LLM-generated code that includes appropriate details regarding end user practices and/or intent corresponding to the code. The labels and descriptions can be made accessible in a format that can be easily parsed by a machine or a program, such as an LLM or other machine learning model. As appropriate, the description of a code in the repository can be augmented by including its relative occurrence and other relevant scores. Implementation of the techniques can enhance the utility, efficiency, and user experience (UX) of developing software solutions with LLM-generated code.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Pearl, David, "Repository of Standard Annotation Codes to Annotate LLM-generated Software Code", Technical Disclosure Commons, (August 07, 2025)
https://www.tdcommons.org/dpubs_series/8437