Expert-Assisted GenAI Response Adaptation for Reliable Answers
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Solution Overview
Problem
Existing GenAI systems require significant interaction and iteration to achieve accurate responses, necessitating a system that allows real-time human input for improved accuracy and reliability.
Innovation Solution
A system where Subject Matter Experts (SMEs) evaluate and adapt AI-generated responses in real-time, incorporating feedback to refine the AI system continuously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GenAI systems use automated reinforcement learning to adapt responses, then the system can operate autonomously, but it requires significant interaction and iteration to achieve acceptable accuracy
Solution Approach 1:
The patent introduces subject matter experts as intermediaries between the GenAI system and the final output. These experts review and refine AI-generated responses in real-time, acting as a mediator that transfers domain knowledge to the system without requiring extensive automated training iterations. This hybrid approach combines AI efficiency with human expertise to achieve accurate responses faster than pure reinforcement learning.
2Extent of automation
If GenAI systems operate autonomously without human input, then automation is maximized, but accuracy and reliability of responses deteriorate
Solution Approach 1:
The patent implements a dynamic system where the level of human involvement adjusts based on query complexity and confidence metrics. For routine queries, the system operates autonomously with high automation. For complex or uncertain queries, the system dynamically transitions to involve subject matter experts, creating a flexible hybrid model that optimizes both automation and reliability based on real-time conditions.
3Reliability
If extensive training data is used to improve GenAI accuracy, then response reliability improves, but the complexity and resources required increase
Solution Approach 1:
The patent enables the system to continuously learn from expert corrections and user feedback without requiring extensive retraining. The feedback integration system automatically incorporates new knowledge into the knowledge base, allowing the GenAI to self-improve over time. This reduces the need for manual retraining and decreases system complexity while maintaining or improving response accuracy through automated continuous learning.
Data Source
AI summary
A system and method for expert-assisted generative AI prompt response adaptation within a computer-populated environment includes a user interface for enabling a user to pose and send user queries and display answers to the user query. A bot answer system is configured to retrieve relevant context in response to the user query. A generative model is configured to provide answers upon the user interface based on retrieved relevant context data in response to the user query. A feedback integration system is configured to provide subject matter expert feedback on at least one of the user query and the answer in real-time. The bot answer system and the feedback integration system are partitioned from one another and direct partitioned data flows through a convergent embedding creation process for storing embeddings in a vector database supporting a knowledge base. The subject matter expert feedback ensures continual improvement of a knowledge base.