Generative AI Output Verification Using Contextual Scoring
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Solution Overview
Problem
Generative AI systems face challenges such as biased or inappropriate content generation, unpredictability, and AI hallucinations, particularly in contexts requiring accuracy and reliability, leading to ethical concerns and loss of trust.
Innovation Solution
A system is developed to verify the outputs of generative AI by generating contextual values based on queries and responses, using a knowledgebase and vector database to determine a verification score, and initiating mitigation actions when the score falls below a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are used to create content based on learned patterns, then creativity and content generation capability are improved, but accuracy and reliability deteriorate due to hallucinations and biased outputs
Solution Approach 1:
The patent introduces a verification system as an intermediary component between the generative AI model and the final output. This verification system checks generated content against the original query and knowledge base, acting as a mediator to filter out hallucinations and biased content while preserving the creative generation capability of the underlying model.
Solution Approach 2:
The patent implements a feedback mechanism where the verification score is calculated by comparing the contextual values of the query, response, and knowledge base. This feedback loop allows the system to identify and correct unreliable outputs by measuring the consistency between generated content and source material, thereby improving overall output accuracy.
2Productivity
If generative AI systems generate content without verification, then productivity and response speed are improved, but harmful factors increase due to biased or incorrect content
Solution Approach 1:
The patent performs verification actions preliminarily by calculating contextual values and verification scores immediately after content generation. This preliminary verification approach allows the system to quickly identify and flag potentially harmful content before it is fully processed or distributed, maintaining productivity while reducing harmful outputs.
3Reliability
If verification mechanisms are added to generative AI systems, then output accuracy is improved, but device complexity increases
Solution Approach 1:
The patent designs the verification system to perform multiple functions using a unified approach. The same contextual value generation and comparison mechanism serves to verify factual accuracy, detect bias, and ensure query-response consistency simultaneously. This multi-functionality reduces the need for separate verification modules for each type of error detection, thereby limiting the increase in system complexity.
Data Source
AI summary
A system and method for improving generative artificial intelligence (AI) software application response is provided. The method includes: receiving a query directed to a generative AI software application; receiving a response to the query, the response generated by the generative AI software application; generating a first contextual value based on the received query; generating a second contextual value based on the received response; generating a verification score based on the first contextual value and the second contextual value; and initiating a mitigation action in response to detecting that the verification score is below a predetermined threshold