LLM Interaction Evaluation With Feedback-Tuned QA Scoring
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Solution Overview
Problem
Conventional quality assurance of agent performance in interactions, such as customer service calls, is laborious and inconsistent due to manual evaluation methods that require reviewing entire recordings and are subject to human variability.
Innovation Solution
A machine learning-based system using a reasoning and answer language model to programmatically evaluate interactions, leveraging a larger LLM for evaluation plan generation and a smaller LLM for efficient evaluation, with continuous fine-tuning based on user feedback to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used to assess agent performance, then human reviewers can provide detailed qualitative feedback, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent introduces an automated evaluation system as an intermediary between the recorded interaction and the quality assurance process. This system uses AI models to analyze interaction transcripts and generate evaluation scores, acting as a mediator that reduces the direct human review workload while maintaining evaluation quality
Solution Approach 2:
The patent replaces the manual mechanical process of human reviewers listening to and analyzing recordings with an automated computational system. The AI-based evaluation system processes interactions programmatically, substituting the mechanical human review process with automated text analysis and scoring algorithms
2Adaptability or versatility
If manual evaluation by multiple human reviewers is performed, then diverse perspectives can be obtained, but inconsistency across different reviewers occurs
Solution Approach 1:
The patent changes the fundamental parameter of evaluation from human judgment to automated AI-based scoring. This parameter change eliminates reviewer variability while maintaining evaluation versatility through configurable evaluation criteria and multiple AI models that can be tuned to different evaluation standards
Solution Approach 2:
The automated evaluation system serves multiple functions: it provides consistent scoring, generates detailed feedback, supports multiple evaluation criteria, and can be applied across different interaction types. This universal system replaces the need for multiple human reviewers while maintaining the ability to assess various aspects of performance
3Measurement precision
If entire audio and text transcripts are reviewed manually, then comprehensive assessment is achieved, but the evaluation process becomes laborious
Solution Approach 1:
The patent replaces manual review of entire transcripts with automated AI-based analysis. The system programmatically processes both audio and text data, extracting relevant information and generating evaluations without requiring human reviewers to manually examine complete transcripts, thereby maintaining comprehensiveness while improving efficiency
Solution Approach 2:
The automated evaluation system extracts only the relevant information needed for assessment from the complete interaction transcripts, rather than requiring reviewers to analyze every detail. This extraction approach maintains comprehensive evaluation while reducing the actual review workload by focusing on key performance indicators
Data Source
AI summary
Machine learning-based evaluation of recorded interactions is disclosed, including: obtaining an evaluation plan to correspond to a new question; retrieving a representative interaction based at least in part on the new question; using a reasoning and answer language model to evaluate the representative interaction against the new question based at least in part on the evaluation plan and to provide a preview evaluation result; outputting, at a user interface, the new question and the preview evaluation result of the representative interaction; receiving, via the user interface, user feedback to the preview evaluation result; updating the reasoning and answer language model based at least in part on the user feedback to the preview evaluation result; and storing a feedback data set including the user feedback.