Contextual Query Tool for Selective AI Data Retrieval
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Solution Overview
Problem
Existing AI chatbots struggle with efficiently identifying and utilizing relevant contextual information for user queries due to the large amount of unrelated user data, leading to inefficient processing and increased resource consumption.
Innovation Solution
A query tool (QT) automatically identifies and retrieves pertinent contextual information by generating intermediate queries to a generative model and a data store, reducing the need for direct user input and minimizing unnecessary data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all user data is provided to the chatbot for processing, then the chatbot has access to comprehensive information, but the processing efficiency decreases and resource consumption increases
Solution Approach 1:
The system extracts only the relevant contextual information needed to answer the user's query from the larger user data set. The query analyzer identifies and retrieves specific contextual data points (such as user preferences, recent activities, or profile information) that are directly related to the current query, rather than processing all available user data. This extraction approach maintains answer completeness while improving processing efficiency.
Solution Approach 2:
The system segments the user data into relevant and irrelevant portions based on the query context. The query analyzer divides the processing task by first analyzing the query to determine what contextual information is needed, then selectively retrieving only those specific segments of user data. This segmentation prevents the chatbot from being overwhelmed by unnecessary data while ensuring all necessary information is available.
2Measurement precision
If comprehensive user data is transferred to the chatbot, then accurate responses can be generated, but data transfer overhead and resource consumption increase
Solution Approach 1:
The query analyzer extracts precisely the contextual information required to accurately answer the user's query. By analyzing the query semantics and matching them against available user data types, the system retrieves only the necessary data points (such as specific user preferences, recent interactions, or profile attributes) rather than transferring comprehensive user data. This extraction maintains response accuracy while minimizing data transfer overhead and computing resource consumption.
3Measurement precision
If the chatbot processes all available user data, then contextual accuracy is improved, but the time required to process queries increases
Solution Approach 1:
The system performs preliminary analysis of the user query to determine what contextual information is needed before actually retrieving and processing the data. The query analyzer pre-identifies the relevant contextual data types and parameters required to answer the query accurately, then selectively retrieves only those specific data points. This preliminary action prevents unnecessary data processing and reduces query response time while maintaining contextual accuracy.
Solution Approach 2:
The query analyzer extracts only the specific contextual information needed to achieve contextual accuracy for the current query. Rather than processing all available user data, the system identifies and retrieves precisely the relevant data points (such as user preferences, recent activities, or profile information) that directly contribute to answering the query. This extraction maintains high contextual accuracy while significantly reducing processing time.
Data Source
AI summary
Techniques and systems are described that perform automated identification and retrieval of contextual information for quick and accurate processing of user queries by artificial intelligence generative models. The techniques include receiving a natural language (NL) query associated with a user identifier (ID) and obtaining, using a first NL generative model, contextual data that is pertinent to the NL query and is associated with the user ID. The techniques further include generating an augmented NL query that is based on the NL query and the contextual data. The techniques include communicating the augmented NL query to a recipient that includes the first NL generative model, a second NL generative model, or a user session associated with the user ID.