BAYESIAN GRAPH-BASED RETRIEVAL-AUGMENTED GENERATION WITH SYNTHETIC FEEDBACK LOOP (BG-RAG-SFL)

Smart overview of the Invention

The invention details an advanced AI system, Bayesian Graph-Based Retrieval-Augmented Generation with Synthetic Feedback Loop (BG-RAG-SFL). This system combines several sophisticated techniques to enhance the performance of large language models (LLMs). It integrates Bayesian evaluation, graph-based retrieval, and synthetic data feedback, creating a platform that continuously improves itself. The system is designed to manage complexities across multiple models, ensuring optimal performance.

Key Features

BG-RAG-SFL incorporates several key components to achieve its objectives:

• Knowledge Graph-Based RAG System: Utilizes a graph-based approach for retrieval-augmented generation.
• Bayesian Evaluation Network: Applies Bayesian methods to evaluate and verify the quality of retrieved data.
• Secondary Ground-Truth Graph: Provides a verification layer to ensure data accuracy and reliability.
• Synthetic Data Generation: Continuously generates data to refine and enhance the AI models.
• Multi-Agent Verification System: Employs multiple agents to verify and validate the processes and outputs.

Functionality

The system also serves as an AI operating system, capable of acting as a virtual user with screen I/O control. It can manage multiple computers, functioning as an intelligent process automation system. This capability allows the system to automate complex processes and improve efficiency in various applications.

Relation to Prior Art

BG-RAG-SFL builds upon existing technologies, addressing limitations found in prior art such as the lack of effective post-processing in retrieval-augmented generation systems. By employing a Bayesian approach, it enhances the quality of text chunks used in generating responses, overcoming issues like conflicting information and improving decision-making capabilities.

Innovative Approach

The system's innovative approach lies in its ability to leverage Bayesian inference and graph-based methods to enhance retrieval-augmented generation. By continuously incorporating synthetic feedback, it ensures that the AI models are not only accurate but also adaptable to new data and contexts, setting a new standard for AI-driven solutions.