Personalized AI Action Graph Inference for Context-Aware Responses
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Solution Overview
Problem
Existing large language models (LLMs) lack the ability to provide personalized responses based on user-specific information and real-time data due to their training methods focusing on general language usage and inability to remember continuous interactions.
Innovation Solution
A personalized AI inference device that generates and executes an action graph using user characteristic information to provide tailored responses, incorporating actions defined by lambda functions and data flows, enabling access to external services and LLMs to gather necessary information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLMs are trained on vast amounts of general text data to provide highly relevant natural language outputs, then language understanding and general knowledge are improved, but the ability to provide personalized responses based on individual user information deteriorates
Solution Approach 1:
The system segments the response generation process into two distinct components: a retrieval component that accesses pre-vectorized user-specific documents from a vector database, and a generation component (LLM) that synthesizes the retrieved information with the query. This segmentation allows the LLM to maintain its general language understanding while the retrieval component provides personalized user information.
Solution Approach 2:
The vector database acts as an intermediary between the user's personal information and the LLM. User documents are pre-processed and stored in the vector database, which then retrieves relevant information based on the query and provides it to the LLM for response generation. This intermediary enables personalized responses without requiring the LLM itself to store or process individual user data.
2Loss of information
If RAG performs similarity measurement searches in pre-indexed databases to find relevant documents, then access to specialized knowledge is improved, but the sophistication of personalized responses deteriorates
Solution Approach 1:
The system changes the search parameters from simple query-based similarity measurement to a more sophisticated approach that incorporates extracted user characteristics. The RAG process is enhanced to consider user-specific attributes (such as preferences, behavior patterns, or demographic information) as additional search parameters, thereby improving both the retrieval accuracy and the personalization quality of responses.
3Productivity
If LLMs process each session independently without remembering continuous interactions, then computational efficiency is improved, but the ability to provide context-aware personalized responses deteriorates
Solution Approach 1:
User-specific information and interaction history are pre-processed and stored in the vector database before actual queries are processed. This preliminary action allows the system to quickly retrieve relevant user context during interactions without requiring complex real-time processing, thereby maintaining computational efficiency while enabling context-aware personalized responses.
Data Source
AI summary
The present disclosure relates to a personalized AI inference device having at least one processor. The processor receives a query from a user, obtains user characteristic information, and generates an action graph including a plurality of nodes and a plurality of edges between the plurality of nodes. A node of the plurality of nodes corresponds to a function defined to be executable by a computer. An edge of the plurality of edges represents a data flow between connected nodes. A value of incoming edge of the node is input data of the function. A value of outgoing edge of the node is output data of the function. The action graph is a directed acyclic graph. The processor outputs a response to the query by executing the functions included in the generated action graph in an order defined by the generated action graph.