Document-Grounded Scoped AI Querying for Repetitive Questions
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Solution Overview
Problem
Repetitive questions in organizational settings lead to valuable time loss, inaccurate responses, and excessive network resource consumption due to reliance on human interaction for information retrieval from publicly available documents.
Innovation Solution
Implementing a machine learning model trained on a specific set of documents accessible by a chatbot to respond to natural-language queries, reproducing the tone and voice of the user, thereby providing automated responses based on the designated documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human interaction is used for information retrieval from publicly available documents, then accurate responses can be obtained, but valuable time is lost and network resources are excessively consumed
Solution Approach 1:
The system performs preliminary actions by training a machine learning model in advance on relevant documents and data. This pre-trained model can then quickly respond to queries without requiring real-time human interaction or extensive document searching, thus reducing time consumption while maintaining response accuracy through the pre-acquired knowledge.
Solution Approach 2:
The system creates a copy of human expertise by training an artificial intelligence model to replicate the knowledge and response patterns found in documented information. This AI copy can answer queries independently without requiring the original human expert's time, yet maintains accuracy by learning from comprehensive document sets.
2Measurement precision
If human interaction is used for information retrieval, then accurate responses can be obtained, but network resource consumption increases
Solution Approach 1:
The system performs document processing, analysis, and knowledge extraction in advance during the model training phase. This preliminary processing consolidates network resource usage into an initial intensive phase, rather than requiring continuous network access and processing for each query, thereby reducing overall network resource consumption while maintaining response accuracy.
Solution Approach 2:
By creating an AI model that copies and internalizes knowledge from documents, the system eliminates the need for repeated network queries to human experts or document repositories. The copied knowledge resides locally in the model, reducing network traffic and resource consumption while preserving response accuracy.
3Reliability
If repetitive questions are handled through human interaction, then accurate responses are provided, but valuable time of knowledgeable members is lost
Solution Approach 1:
The system enables self-service by allowing the AI model to independently handle repetitive queries without requiring human intervention. The model serves itself by drawing from its trained knowledge base to provide reliable responses to common questions, freeing knowledgeable members to focus on more complex tasks and improving overall productivity while maintaining response reliability.
Solution Approach 2:
The system creates an AI copy of organizational knowledge that can independently handle repetitive questions. This copy maintains the reliability of human expertise by learning from comprehensive document sets, while simultaneously protecting knowledgeable members' time by automating routine query responses.
4Productivity
If automated systems are implemented for query response, then time and resources are saved, but the system complexity increases
Solution Approach 1:
The system replaces complex mechanical human interaction processes with an automated machine learning model. Instead of requiring human experts to manually search documents and formulate responses, the AI model automatically processes queries using its trained knowledge, simplifying the operational complexity while improving information retrieval efficiency.
Data Source
AI summary
Techniques for scoped querying using customized artificial intelligence are provided. In an example method, a computing device determines a set of documents about a user of a first client device of a plurality of client devices. The computing device integrates the set of documents into a datastore, the datastore accessible by a machine learning model trained to respond to natural-language queries in the context of the set of documents. The computing device receives, from a second client device of the plurality of client devices, a query about the first user of the first client device and then receives, from the machine learning model, a response to the query based on the set of documents. The computing device outputs the response to the second client device.