Content-Based Validation Using LLMs for Digital Adoption Platforms
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Solution Overview
Problem
Digital adoption platforms (DAPs) lack adequate text analysis and generation capabilities to provide content-based support and guidance for input validation in digital tasks, limiting their ability to assist users effectively.
Innovation Solution
Integrating Large Language Models (LLMs) with DAP building blocks, such as validation tooltips, to enable content-aware validation processes. This involves generating prompts that incorporate user input and validation rules, processed by LLMs to provide feedback and suggestions for compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional validation methods are used in digital adoption platforms, then the system structure remains simple, but the text analysis and generation capabilities are insufficient to provide content-based support and guidance
Solution Approach 1:
The patent introduces Large Language Models (LLMs) as an intermediary component between the user input and the validation logic. The LLM receives the input text and validation rules, processes them through natural language understanding, and generates compliance feedback. This intermediary enables sophisticated text analysis capabilities while maintaining a relatively simple overall system architecture, as the LLM handles the complex processing internally.
2Measurement precision
If LLMs are integrated to provide content-aware validation, then the validation accuracy and user support quality improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the validation rules and structuring them in a format optimized for LLM processing. The prompt template is pre-configured with the validation rules and field information, so that when validation is needed, the LLM only needs to process the user input against the pre-structured criteria, reducing the actual processing time during validation operations.
3Ease of operation
If LLMs are used for real-time validation feedback, then the quality of guidance and suggestions improves, but the computational energy consumption increases
Solution Approach 1:
The patent extracts only the essential elements needed for validation from the user input and context, forming a concise prompt for the LLM. Instead of processing entire pages or applications, the system extracts specifically the field input, validation rules, and relevant context, reducing the computational burden on the LLM and thereby lowering energy consumption while maintaining guidance quality.
Data Source
AI summary
A method, product and apparatus implemented at an end device of an administrator user, comprising: selecting a field in a page of a third-party application; defining a trigger event for identifying that an end user entered input to the field; defining an automation process to be executed in response to the trigger event, said defining comprises defining a validation rule using free text in natural language, the automation process is configured to generate a prompt to a Generative Artificial Intelligence (AI) engine, the prompt comprising the validation rule and instructions to determine whether the input complies with the validation rule; and defining a configuration for presenting an output from the Generative AI engine over the page.