Context-Aware RAG for Personalized Infrastructure AI Responses
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Solution Overview
Problem
Existing generative AI systems lack the capability to provide highly customized and personalized responses to user queries about their infrastructure, as they primarily rely on enterprise knowledge bases without incorporating user-specific contextual data and analytics.
Innovation Solution
A generative AI system that combines user contextual data, such as real-time metrics and configuration data, with an enterprise knowledge base to create personalized responses, using retrieval augmented generation (RAG) and large language models (LLM) to provide detailed answers and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing generative AI systems rely on enterprise knowledge bases, then they can provide general information, but they cannot provide highly customized and personalized responses to user queries about their specific infrastructure
Solution Approach 1:
The patent combines enterprise knowledge base information with user-specific contextual data and analytics to create a hybrid information system. The RAG architecture merges general domain knowledge from the knowledge base with personalized user infrastructure data, enabling the system to provide both broadly accurate and highly customized responses simultaneously.
Solution Approach 2:
The system performs preliminary actions by pre-processing user contextual data and analytics before generating responses. It retrieves and prepares relevant user-specific information in advance, converting it into usable formats that can be quickly integrated with knowledge base content during query processing.
2Adaptability or versatility
If generative AI systems use only enterprise knowledge bases, then the system architecture remains simple, but the response customization and personalization are limited
Solution Approach 1:
The patent segments the information system into distinct functional modules: a knowledge base component, a user contextual data component, an analytics component, and a RAG integration layer. This segmentation allows each component to specialize in specific tasks while maintaining overall system manageability and scalability.
Solution Approach 2:
The RAG architecture serves as an intermediary layer that bridges the enterprise knowledge base and user-specific contextual data. This mediator integrates information from multiple sources, reconciles different data formats, and synthesizes unified personalized responses without requiring direct complex interactions between all system components.
3Measurement precision
If the system incorporates user contextual data and analytics, then personalized responses are achieved, but the data processing and integration complexity increases
Solution Approach 1:
The system applies parameter changes by transforming user contextual data and analytics into standardized formats suitable for RAG processing. It converts diverse data types (metrics, logs, configuration data) into uniform representations that can be efficiently retrieved and integrated, maintaining data fidelity while simplifying processing operations.
Data Source
AI summary
A system can receive user input data that comprises an identification of a computer infrastructure component of a group of computer infrastructure components that are associated with a user account. The system can identify information about the group of computer infrastructure components based on the identification of the computer infrastructure component. The system can convert text of the user input data into a first numerical vector. The system can create a context of the user input data based on identifying a match between the first numerical vector and a second numerical vector of a group of numerical vectors, wherein the context corresponds to a natural-language version of the second numerical vector. The system can combine the context and the information about the group of computer infrastructure components into a combined context, and input the combined context and the user input data to a large language model to produce a result.