LLM Action Mapping From Character Vectors for Output Validation
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Solution Overview
Problem
Existing software development systems lack intuitive, consistent, and reliable methods for selecting generative machine learning models and validating their outputs, leading to inefficiencies, security risks, and compliance challenges due to sub-optimal model selection, manual rule application, and inability to adapt to evolving regulatory requirements.
Innovation Solution
A data generation platform that dynamically evaluates machine learning prompts for model selection and output validation, using generative AI models to map gaps in controls to operative standards and identify actionable items from guidelines, ensuring secure, flexible, and modular control over LLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual rule application is used for model selection and output validation, then system complexity is reduced, but productivity and reliability deteriorate due to inefficiencies and human error
Solution Approach 1:
The system enables automated self-service through AI models that autonomously select appropriate generative models and validate their outputs without human intervention. The platform automatically evaluates prompts, selects optimal models, generates outputs, and validates results, replacing manual rule application with intelligent automation that improves both productivity and reliability while maintaining manageable complexity through systematic design
Solution Approach 2:
Manual mechanical processes of model selection and validation are replaced with intelligent AI-based systems. The platform uses large language models and generative AI to automatically perform model selection, output generation, and validation tasks that were previously handled through manual rule application, thereby eliminating human error and inefficiencies while maintaining system accessibility
2Ease of operation
If manual rule application is used for output validation, then ease of operation is maintained, but reliability deteriorates due to security risks and compliance challenges
Solution Approach 1:
Manual output validation is replaced with automated AI-based validation systems that maintain operational simplicity while dramatically improving reliability. The platform automatically validates generated outputs for security compliance, accuracy, and regulatory adherence, eliminating human error and bias while keeping the system easy to operate through automated processes that require minimal user intervention
Solution Approach 2:
The system implements automated feedback loops where AI models continuously validate outputs against security requirements and compliance standards. The validation results feed back into the model selection and output generation processes, enabling continuous improvement and ensuring consistent reliability without complicating user operations
3Device complexity
If static model selection methods are used, then device complexity is reduced, but adaptability deteriorates due to inability to adapt to evolving regulatory requirements
Solution Approach 1:
The system transitions from static model selection to dynamic, adaptive model selection that automatically adjusts to evolving regulatory requirements. The platform uses AI models to continuously evaluate prompts and select appropriate generative models based on current regulatory landscapes, enabling the system to adapt to changing requirements without increasing operational complexity for users
4Ease of operation
If sub-optimal model selection is used, then ease of operation is improved, but manufacturing precision deteriorates due to inconsistent and unreliable results
Solution Approach 1:
Manual model selection is replaced with intelligent AI-based model selection that automatically identifies and selects the most appropriate generative models for each prompt. The system evaluates prompts using large language models and selects optimal models based on task requirements, ensuring consistent and reliable outputs while maintaining ease of operation through automated selection processes that require minimal user input
Data Source
AI summary
The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and/or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.