LLM Task Template Validation for Enterprise Process Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Creating and maintaining enterprise process content is difficult due to the need for specialized knowledge, manual effort, and human interaction, making it challenging to configure and automate enterprise tasks effectively.
Innovation Solution
A system utilizing a large language model to generate, validate, and correct content for process automation by structuring a domain model into tasks, performing schema validation, and enriching templates with backend data, with iterative corrections until validation is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual creation and maintenance of enterprise process content is used, then specialized knowledge and human interaction are required, but the process is difficult and time-consuming
Solution Approach 1:
The system implements automated feedback loops where the LLM generates content, the validation system checks it against schemas and business rules, and corrections are automatically applied. This feedback mechanism enables automated content generation while managing complexity through structured validation layers.
Solution Approach 2:
The system performs self-validation and self-correction by automatically checking generated content against defined schemas and business rules, then applying corrections without manual intervention. This self-service capability reduces the need for specialized human expertise while maintaining content quality.
2Reliability
If iterative correction process is implemented, then data integrity is improved, but processing time increases
Solution Approach 1:
The system performs preliminary validation checks during the generation phase rather than after completion. By validating content structure and format early in the process, the system reduces the number of iterative corrections needed, thereby improving data integrity while minimizing processing time.
Solution Approach 2:
The system replaces manual, mechanical validation processes with automated LLM-based validation that can instantly check content against complex schemas and business rules. This substitution dramatically reduces processing time while maintaining or improving data integrity through more thorough and faster validation.
3Manufacturing precision
If schema validation is performed, then content accuracy is improved, but system complexity increases
Solution Approach 1:
The validation system is segmented into multiple independent validation layers: schema validation for structure, business rule validation for logic, and data type validation for format. This segmentation allows each validation layer to be simpler and more focused, making the overall complex validation process manageable and maintainable.
Data Source
AI summary
Arrangements for intelligent content generation for process automation are provided. A domain model, being structured into tasks according to a defined schema, may be exported for processing by a large language model. A prompt and a context window associated with a task of the domain model may be received. A task template associated with the task may be modified. The modified task template may be enriched with data from a backend system. Content validation may be performed on content of the modified task template enriched with the data from the backend system. Schema validation may be performed for validating the modified task template enriched with the data from the backend system against the defined schema. Correction of invalid tasks may be performed in an iterative loop until the modified task template enriched with the data from the backend system is validated. Then, changes to the domain model may be applied.