Adaptive RAG Chatbot Feedback Classification for Knowledge Updates
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Solution Overview
Problem
Conventional Retrieval-Augmented Generation (RAG) systems suffer from architectural and computational limitations, failing to adapt effectively to user needs and feedback, with static knowledge bases and rigid prompt systems that require resource-intensive manual updates or retraining, and LLMs face challenges in classification tasks with unpredictable computational behavior.
Innovation Solution
A feedback-driven, adaptive AI chatbot system that dynamically updates its knowledge base and prompts through a two-stage classification process using a decision tree classifier to process user feedback, generating new documents that refine contextual information or behavioral instructions, and employs a hybrid ranking system for document retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional RAG systems use static knowledge bases and rigid prompt systems, then system architecture is simple, but adaptability to user needs and feedback deteriorates
Solution Approach 1:
The patent implements dynamic knowledge bases and prompt systems that automatically adapt to user feedback through classification and document generation. The system transitions from static to dynamic by continuously updating knowledge bases based on classified feedback types (factual errors, incomplete information, tone/style issues) and generating appropriate correction documents, thereby resolving the contradiction between adaptability and complexity.
Solution Approach 2:
The patent establishes a feedback loop where user feedback is systematically processed through classification (using decision trees and LLMs) to identify feedback types, generate correction documents, and update knowledge bases. This structured feedback mechanism enables continuous adaptation while managing complexity through automated processing pipelines.
2Reliability
If manual updates or retraining are used to improve knowledge bases, then knowledge accuracy is improved, but resource consumption and time requirements worsen
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically processes user feedback through classification and document generation without requiring manual intervention. The automated pipeline classifies feedback types, generates correction documents, and updates knowledge bases autonomously, reducing both resource consumption and time requirements while maintaining knowledge accuracy.
Solution Approach 2:
The patent employs preliminary classification of feedback into distinct types (factual errors, incomplete information, tone/style issues) before document generation. This preliminary organization enables more efficient processing by routing feedback through appropriate processing paths, reducing overall computational resource consumption while maintaining accuracy.
3Difficulty of detecting and measuring
If LLMs are used for classification tasks, then classification capability is improved, but computational behavior predictability deteriorates
Solution Approach 1:
The patent segments the classification task into distinct feedback types (factual errors, incomplete information, tone/style issues) and processes each type through specialized document generation paths. This segmentation improves classification capability by focusing on specific feedback categories while enhancing predictability through standardized processing routines for each segment.
Solution Approach 2:
The patent introduces decision trees as intermediary structures between LLM-based feedback analysis and document generation. The decision trees provide a deterministic framework that translates LLM classifications into predictable processing paths, thereby maintaining classification capability while improving computational behavior predictability.
4Productivity
If feedback loops are automated through classification and document generation, then system adaptability is improved, but processing time and complexity worsen
Solution Approach 1:
The patent performs preliminary classification of feedback into distinct types before document generation, enabling more efficient processing by routing each feedback type through appropriate specialized paths. This preliminary organization reduces overall processing time by avoiding generic processing workflows while maintaining comprehensive feedback handling.
Solution Approach 2:
The patent changes the processing parameters based on feedback type classification, applying different document generation strategies and processing intensities for different feedback categories. This parameter adaptation optimizes processing efficiency by allocating computational resources appropriately while reducing overall processing time through targeted rather than uniform processing.
Data Source
AI summary
A system for dynamically adapting a conversational artificial intelligence (AI) system includes a chatbot system, a feedback and classifier unit, and a document generator. The chatbot system generates a response to a user query. The feedback and classifier unit receives the user query, a large language model (LLM) provided response, and system architect provided feedback to create a data object. The unit retrieves a set of ternary questions from a questions database and processes the data object using the LLM to generate answers, creating a feature vector of ternary answers. It then determines a classification label for the feedback by processing the feature vector with a decision tree, where the label indicates a knowledge or behavioral update. The document generator creates a new document based on the classification label and feedback and updates a knowledge base or prompts database with the new document based on the determined classification label.