Query Tool Context Extraction for Efficient Generative AI Processing
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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 decreased user satisfaction.
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, using natural language processing to determine necessary data and generate context-based queries for accurate responses.
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 computing resources are wasted on unrelated data
Solution Approach 1:
The system extracts only the relevant contextual information needed to answer the user's query from the larger dataset of user data. The query analyzer identifies and retrieves specific contextual data points that are directly related to the query intent, excluding unrelated information. This extraction approach ensures the chatbot receives sufficient context while avoiding processing overhead from irrelevant data.
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 into identifying query intent, determining required context types, retrieving specific contextual data, and excluding unrelated information. This segmentation allows efficient processing by handling only the necessary data segments.
2Measurement precision
If comprehensive user data is analyzed to determine query context, then accurate contextual understanding is achieved, but the time required for processing increases
Solution Approach 1:
The system performs preliminary actions by pre-identifying and organizing user contextual data into accessible formats before queries are submitted. The query analyzer has ready access to structured user data that can be quickly retrieved based on query intent, eliminating the need for comprehensive real-time analysis of all user data during query processing.
Solution Approach 2:
The system extracts only the specific contextual information needed for accurate query understanding without analyzing the entire user dataset. The query analyzer identifies and retrieves precise contextual data points relevant to the query, achieving accurate contextual understanding while minimizing processing time by excluding unrelated data analysis.
3Productivity
If a simple query processing approach is used, then computing resources are saved, but the accuracy of responses decreases due to lack of contextual information
Solution Approach 1:
The query analyzer serves as an intermediary component between the user query and the chatbot processing system. It retrieves and provides the necessary contextual information to the chatbot, ensuring accurate responses without requiring the chatbot to process all user data. This intermediary approach maintains response accuracy while preserving computing resource efficiency.
Solution Approach 2:
The system extracts and provides only the essential contextual information needed for accurate query response. The query analyzer identifies and retrieves specific contextual data points that directly contribute to response accuracy, excluding unrelated information that would consume computing resources. This extraction ensures both accuracy and efficiency are maintained.
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.