AI Query Amplification System for Response Precision
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Solution Overview
Problem
Generative artificial intelligence models for natural language processing face challenges such as 'model fatigue' and variability in responses due to external parameters like computing power, and dependency on the quality and length of the initial query, leading to inconsistent and less accurate responses.
Innovation Solution
A system that uses a generative artificial intelligence model to provide more precise and comprehensive responses by amplifying the input query with preset rules, reducing dependency on computing power, and accumulating responses to form a global query response, while also allowing for different conceptual query approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the initial query is short or vague, then the query input is simple and quick to provide, but the response precision and completeness deteriorate
Solution Approach 1:
The system performs preliminary actions by automatically expanding and enriching the user's query before processing. It identifies key entities and concepts in the original query, then generates additional relevant information and context to create a more comprehensive query input, ensuring high response precision without requiring complex user input
2Adaptability or versatility
If the generative artificial intelligence model processes queries with variable computing power, then the model can adapt to different resource conditions, but the response consistency deteriorates due to model fatigue
Solution Approach 1:
The system segments the query processing into distinct phases: query expansion, entity recognition, context generation, and response synthesis. Each segment can be independently optimized and executed with appropriate computational resources, reducing the cumulative fatigue effect while maintaining overall processing quality and response consistency
Solution Approach 2:
The system introduces an intermediary query expansion module that acts as a buffer between the user's simple query and the generative model. This intermediary enriches the query with additional context and structured information, allowing the model to process more consistent and reliable responses regardless of varying computing power conditions
3Measurement precision
If a detailed and lengthy query is provided, then the response precision and comprehensiveness are improved, but the query input complexity and time requirement increase
Solution Approach 1:
The system implements self-service by automatically performing query expansion and enrichment tasks that would otherwise require significant user input. The system identifies missing context, adds relevant entities, and structures the query automatically, saving user time while achieving comprehensive response quality
Data Source
AI summary
A system for querying interpreted content from a generative artificial intelligence model is disclosed. The system includes a generative artificial intelligence model and one or more processors. The model is configured to accept a natural language text input and produce a human-like text response. The system performs operations including receiving a natural language text input representative of a query subject and a query context, receiving an input representative of a query response structure, amplifying a combination of the inputs, providing the amplified query to the model, receiving the human-like text response generated by the model, accumulating the responses to form a global query response, and providing the global query response to a computerized interface.