LLM Prompt Mediation for Faster Code and Unit Test Generation
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Solution Overview
Problem
Software development is a time-consuming process, and using large language models (LLMs) for software development requires practice and time, limiting their initial utility and efficiency.
Innovation Solution
A software development efficiency improvement engine utilizing LLMs generates source code and unit tests by providing validated prompts, post-processing outputs, and integrating with a neural network architecture to enhance development efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs are used for software development tasks, then code generation capability is improved, but the time and practice required to operate LLMs effectively increases
Solution Approach 1:
The patent introduces an intermediary system that sits between the developer and the LLM, automatically handling prompt construction, parameter optimization, and output validation. This mediator translates simple developer intent into optimized LLM interactions, eliminating the need for developers to learn LLM operation skills while maintaining high code generation capability.
Solution Approach 2:
The system performs preliminary actions by pre-configuring optimal prompts, parameters, and validation rules before LLM interaction. By preparing these elements in advance and caching successful interaction patterns, the system eliminates the need for real-time adjustment and practice, allowing immediate effective use of LLMs for code generation.
2Speed
If LLMs are used for software development, then code generation speed is improved, but the quality and reliability of generated code deteriorates without expert operation
Solution Approach 1:
The patent implements multi-layer feedback mechanisms including automated code validation, compilation checking, and quality metric evaluation. The system continuously monitors generated code quality and adjusts LLM parameters and prompts in real-time based on feedback, ensuring high reliability while maintaining fast generation speeds through iterative refinement.
Solution Approach 2:
The system enables self-service by incorporating automated validation, testing, and refinement capabilities that allow the LLM to self-correct and improve its own output. The generated code undergoes automatic quality checks and refinement cycles without requiring expert human intervention, maintaining both speed and reliability.
3Manufacturing precision
If complex prompts are used to improve LLM output quality, then code quality improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the prompt construction process into modular, reusable components such as template structures, parameter sets, and validation rules. Each component can be independently configured and validated, reducing overall system complexity while enabling high-quality code generation through systematic composition of these standardized elements.
Data Source
AI summary
In a first use case, a description of source code to be created is validated and used to form a prompt for a large language model (LLM). The prompt is provided to the LLM to generate an output. The output from the LLM is post-processed to generate the source code. In a second use case, a source code file is processed and provided, along with instructions, to an LLM. The LLM provides a list of the methods in the source code file. The LLM is requested to provide a unit test for each of the listed methods. The output from the LLM is post-processed to generate unit tests for the methods in the identified source code file. In a third use case, the LLM is used to generate unit tests for new methods added to the source code file after unit tests were generated.