Process Artifact Analysis for Self-Service AI Application Generation
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Solution Overview
Problem
Current software development technologies require professional developers to embed process artifacts into software applications, limiting inexperienced developers' ability to effectively and efficiently build software applications.
Innovation Solution
A computer-implemented method using retrieval augmented generation based on process artifacts, which involves analyzing metadata of artifacts through an analyzer component, generating prompts for a large language model to create software application pages, and deploying the application via a software development platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If professional developers manually embed artifacts into software applications, then code integration quality is improved, but development time and complexity increase
Solution Approach 1:
The patent replaces manual mechanical code writing and embedding processes with an automated AI system. The analyzer component automatically extracts metadata from artifacts, the prompt generator creates structured prompts, and the large language model generates integrated code, eliminating the need for manual developer intervention in these tasks.
Solution Approach 2:
The system enables self-service code generation where the artifact metadata and analysis data automatically serve as inputs for code generation. The large language model processes the analysis data and generates appropriate code without requiring professional developer expertise, allowing inexperienced developers to build applications independently.
2Adaptability or versatility
If professional developers write code to integrate artifacts, then software application functionality is improved, but device complexity and skill requirements increase
Solution Approach 1:
The patent introduces an intermediary system consisting of the analyzer component, prompt generator, and large language model. This intermediary layer translates complex artifact metadata into structured prompts that the large language model can process, generating functional code without requiring developers to manually write complex integration code.
Solution Approach 2:
The system changes the parameters of code generation by using structured prompt formats that include metadata, analysis data, and generation instructions. This parameterized approach allows the large language model to generate appropriate code for different artifact types by simply changing the input parameters rather than requiring different code generation logic.
3Reliability
If manual code generation is used, then control over generated code is improved, but productivity decreases
Solution Approach 1:
The patent implements feedback mechanisms where the analyzer component processes artifact metadata to generate analysis data, which then feeds into the prompt generator to create structured prompts. This feedback loop ensures that the generated code accurately reflects the artifact structure and requirements, maintaining code control accuracy while accelerating development.
Solution Approach 2:
The system performs preliminary analysis of artifact metadata before code generation. The analyzer component extracts and analyzes metadata to create structured analysis data in advance, which then serves as the basis for code generation. This preliminary action ensures that the generated code is accurate and tailored to the specific artifact requirements.
Data Source
AI summary
A computer-implemented method may comprise obtaining a configuration of a process created via a software development platform, where the configuration of the process comprises a plurality of artifacts, generating analysis data for an artifact in the plurality of artifacts based on metadata of the artifact, and generating a page generation prompt based on the metadata of the artifact and the analysis data for the artifact, where the page generation prompt is configured to instruct a large language model to generate a page of a software application. The computer-implemented method may further comprise obtaining a metadata file for the page of the software application based on the page generation prompt using the large language model, and providing the metadata file for the page of the software application to the software development platform.