GAI Accuracy Diagnosis Using RAG Shape Multiplets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing generative artificial intelligence (GAI) models struggle with accuracy due to their reliance on outdated training data, leading to incorrect responses when faced with information outside their scope, and existing Retrieval-Augmented Generation (RAG) techniques fail to effectively identify and address the root causes of inaccuracy.
Innovation Solution
The method employs Retrieval-Augmented Generation (RAG) shapes, using c-metrics to generate and analyze RAG shape multiplets, particularly RAG shape triplets and doublets, to identify the root cause of inaccuracy in GAI models by computing c-scores and generating graphical representations to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If GAI models use outdated training data, then model training cost is reduced, but accuracy deteriorates when facing information outside training scope
Solution Approach 1:
The system pre-computes c-scores for multiple c-metrics and generates RAG shape multiplets in advance to identify potential accuracy issues before they manifest as incorrect responses. This preliminary analysis enables proactive model improvement without requiring reactive retraining.
Solution Approach 2:
RAG shapes serve as an intermediary representation that bridges the gap between raw model outputs and actionable insights. By transforming model performance data into visual RAG shape multiplets, the system enables intuitive identification of accuracy root causes without direct model retraining.
2Manufacturing precision
If existing RAG techniques are used to address inaccuracy, then some accuracy improvement is achieved, but root cause identification capability remains insufficient
Solution Approach 1:
The system transitions from traditional linear metric analysis to multi-dimensional RAG shape multiplet visualization. By representing c-metrics as overlapping shapes in a visual space, the system enables simultaneous analysis of multiple accuracy dimensions and their interrelationships, making root cause detection intuitive and efficient.
Solution Approach 2:
The RAG shape visualization employs color-coding to represent different c-metric performance levels and their relationships. This visual encoding transforms abstract accuracy metrics into immediately interpretable visual signals, enabling rapid root cause identification without complex analysis.
3Manufacturing precision
If GAI models are retrained to improve accuracy, then model performance improves, but time consumption and computational resources increase
Solution Approach 1:
The system extracts only the specific c-metrics and their relationships that contribute to accuracy issues, represented as RAG shape multiplets. By focusing analysis on these extracted key elements rather than retraining the entire model, the system achieves targeted improvements with minimal time and resource investment.
Solution Approach 2:
The system changes the approach from model parameter updates (retraining) to performance metric analysis (c-scores and RAG shapes). By operating at the metric level rather than the model parameter level, the system enables rapid accuracy improvement through targeted interventions based on identified root causes.
Data Source
AI summary
A method, computer program product, and computer system for improving accuracy of results obtained from use of a generative artificial intelligence (GAI) model. The GAI model is executed to generate Q answers to Q questions Q prompts, respectively. A c-score is computed for each c-metric of multiple c-metrics of the Q questions. The computed c-score exceeds zero for X c-metrics of the multiple c-metrics. X Retrieval-Augmented Generation (RAG) shapes respectively corresponding to the X c-metrics are generated. Multiple RAG shape multiplets and associated multiplet scores are determined using the X RAG shapes. A top RAG shape multiplet having a highest RAG multiplet score is selected from the multiple RAG shape multiplets. The top RAG shape multiplet is graphically displayed on a display device. The GAI model's accuracy is improved after a root cause of the GAI model's inaccuracy was identified from the graphically displayed top RAG shape multiplet.