AI Customer Service Q&A Expansion Through Expert-Scored Retraining
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Solution Overview
Problem
Traditional AI customer service systems lack the ability to continuously learn and adapt to new customer inquiries and market demands, leading to reduced answer quality and limited application potential in complex scenarios.
Innovation Solution
An expandable construction method for an AI-based customer service system that automatically generates a question and answer report, evaluates its quality using an expert AI model, and determines whether to incorporate it as training material based on an evaluation score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional AI customer service systems use manually labeled dialogue samples for training, then basic dialogue capabilities can be established during early stages, but the system lacks the ability to continuously learn and adapt to new customer inquiries and market demands
Solution Approach 1:
The system automatically generates question and answer reports from customer service interactions and uses an expert AI model to evaluate and select high-quality samples for retraining. This self-service mechanism enables continuous learning without manual intervention, allowing the system to adapt to new customer inquiries and market demands autonomously
Solution Approach 2:
The system implements a feedback loop where the expert AI model evaluates generated question and answer reports, scores them based on quality metrics, and feeds the highest-scoring samples back into the training process. This feedback mechanism ensures continuous improvement of the AI model's performance and adaptability
2Reliability
If the AI system continuously learns from new data, then answer quality and dialogue processing capabilities improve, but the system complexity and computational resources required increase
Solution Approach 1:
The expert AI model serves as an intermediary that automatically evaluates and filters generated question and answer reports before they are used for training. This intermediary layer quality-controls the training data, ensuring only high-quality samples are incorporated, thereby maintaining answer quality without requiring complex manual curation processes
Solution Approach 2:
The system performs preliminary evaluation and scoring of question and answer reports using the expert AI model before they are used for training. This preliminary action ensures that only pre-vetted, high-quality samples are incorporated into the training process, maintaining reliability while streamlining the continuous learning workflow
Data Source
AI summary
The disclosure describes an expandable construction method for an artificial intelligence-based customer service question and answer system and a computer system using the same. In the expandable construction method for an artificial intelligence-based customer service question and answer, a customer service question and answer text as test material is inputted to test a question and answer artificial intelligence (AI) model. The question and answer AI model automatically outputs a question and answer report based on the customer service question and answer text. An expert artificial intelligence (AI) model automatically outputs an evaluation score based on the question and answer report. It is determined whether to input the question and answer report into the question and answer AI model to serve as training material based on the evaluation score.