Personalized RAG Retrieval for Accurate User-Specific Responses
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Solution Overview
Problem
Existing retrieval-augmented generation systems (RAGs) suffer from inaccuracy due to overgeneralized knowledge bases and lack of adaptability to specific user accounts, leading to unreliable outputs and inflexibility.
Innovation Solution
A personalized retrieval-augmented generation system that utilizes a personalized embedding model, vector database, and large language model to generate tailored responses by comparing query embeddings with entity-specific data contexts, fine-tuning components based on feedback, and adapting to user-specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wide-ranging database with vast amounts of data across huge variety of topics is used to train large language models, then broad coverage of output generation is achieved, but accuracy deteriorates due to overgeneralization and hallucinations
Solution Approach 1:
The patent segments the knowledge base into multiple specialized databases, each focused on a specific domain or topic area. Instead of using a single wide-ranging database, the system divides knowledge into organized segments that can be selectively accessed based on the query topic, reducing overgeneralization while maintaining broad coverage capability.
Solution Approach 2:
The patent introduces an intermediary retrieval layer between the user query and the large language model. This retrieval system acts as a mediator that fetches accurate, domain-specific information from specialized databases before passing it to the LLM, thereby improving response accuracy without sacrificing the model's broad generative capabilities.
2Device complexity
If a one-size-fits-all framework is used in existing RAGs, then system simplicity is maintained, but adaptability to specific user accounts deteriorates
Solution Approach 1:
The patent implements a dynamic framework that adapts to each user account by retrieving and injecting user-specific contextual information into the LLM prompts. The system dynamically adjusts the retrieval strategy and knowledge base selection based on the specific user's needs, maintaining simplicity through automated adaptation rather than complex manual configuration.
Solution Approach 2:
The patent changes key parameters of the RAG system based on user-specific requirements, including which databases to query, what contextual information to retrieve, and how to weight different information sources. These parameter adjustments enable the system to adapt to different users without requiring fundamentally different system architectures.
3Device complexity
If existing RAGs use generalized knowledge bases, then system complexity is reduced, but response relevance to specific entities deteriorates
Solution Approach 1:
The patent segments the knowledge base into multiple specialized databases organized by domain, topic, or entity type. This segmentation maintains manageable system complexity while enabling precise retrieval of relevant information for specific entities, improving response relevance without requiring an overly complex monolithic knowledge base.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating personal responses through retrieval-augmented generation. In particular, the disclosed systems can generate a query embedding from a query generated by an entity and determine data context specific to the entity by comparing the query embedding with a plurality of vectorized segments of content items associated with the entity. The disclosed systems can provide the data context to a large language model and generate a personalized response informed by the data context. Subsequently, the disclosed systems can provide the personalized response for display on a client device associated with the entity.