AI-Generated Code Tagging to Reduce Non-Deterministic Errors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generative AI models generate both deterministic and non-deterministic code, with non-deterministic code introducing entropy and errors, complicating system performance and error recovery.
Innovation Solution
Identify and tag deterministic and non-deterministic code portions in AI-generated code, providing feedback to the generative AI model to reinforce deterministic outputs and mitigate non-deterministic code, and generate multipathing code for deterministic portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI model generates code without filtering, then code generation speed and productivity are improved, but system reliability deteriorates due to non-deterministic code introducing errors
Solution Approach 1:
The patent segments the generated code into deterministic and non-deterministic portions by inserting tags at specific locations. This segmentation allows the system to identify and handle different code types differently, maintaining generation speed while improving reliability through targeted processing of non-deterministic sections.
Solution Approach 2:
The patent implements a feedback mechanism where the system analyzes generated code, identifies non-deterministic portions through tagging, and provides feedback to the generative AI model. This feedback loop enables the model to learn from previous generations and reduce non-deterministic code in future outputs, gradually improving reliability without sacrificing productivity.
2Adaptability or versatility
If non-deterministic code is present in AI-generated code, then code generation flexibility is maintained, but system error recovery capability deteriorates
Solution Approach 1:
By segmenting code into tagged deterministic and non-deterministic portions, the system enables selective error recovery. When errors occur, the system can focus repair efforts on specific non-deterministic segments while preserving the flexibility of the overall code generation approach.
Solution Approach 2:
The patent extracts non-deterministic code portions through tagging and separation. This extraction allows the system to isolate problematic areas for targeted error recovery while maintaining the flexible nature of the remaining deterministic code, thereby improving ease of repair without sacrificing generation flexibility.
3Reliability
If code is tagged to identify deterministic and non-deterministic portions, then system reliability is improved, but device complexity increases
Solution Approach 1:
The patent uses tagging as a form of visual marking (analogous to color changes) to distinguish deterministic and non-deterministic code portions. This marking mechanism improves reliability by making code characteristics explicit while adding minimal complexity compared to complete code rewriting or analysis.
Solution Approach 2:
Instead of completely analyzing and reprocessing all code, the patent applies partial action by only tagging and processing the non-deterministic portions identified through pattern recognition. This partial approach improves reliability without the excessive complexity of full code transformation.
Data Source
AI summary
Tagging deterministic code in artificial intelligence-generated code, including: receiving code generated by a generative artificial intelligence (AI) model; identifying at least one portion of code by identifying at least one of: one or more portions of deterministic code or one or more portions of non-deterministic code; and tagging the identified at least one portion of code.