Hybrid AI Query Response Grounding to Reduce Hallucinations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automated query response systems struggle to comprehend complex queries, lack integration with critical systems, and suffer from hallucinations, leading to inaccurate responses.
Innovation Solution
A hybrid AI model segments data files into portions, embeds them in a vector space, extracts relevant data, and constrains responses using parametric input prompts based on extracted data, reducing hallucinations and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional automated query response systems use simple keyword matching, then the system operation is simple, but the system fails to comprehend complex queries and provide accurate responses
Solution Approach 1:
The patent segments the query processing into multiple distinct stages: embedding generation, retrieval augmented generation (RAG), and verification. Each stage handles specific aspects of query comprehension, allowing the system to process complex queries accurately without overwhelming complexity at any single stage.
Solution Approach 2:
The patent introduces intermediate components including embedding models that convert queries and documents into vector representations, and a RAG module that retrieves relevant context before generating responses. These intermediaries bridge the gap between simple keyword matching and complex semantic understanding.
2Measurement precision
If AI/ML models are integrated into query response systems, then the system can handle complex queries, but the system suffers from hallucinations and generates incorrect answers
Solution Approach 1:
The patent implements a verification stage where the generated response is checked against the retrieved context and original query. This feedback mechanism detects and corrects hallucinations by ensuring responses are grounded in actual retrieved information rather than model predictions alone.
Solution Approach 2:
The patent performs preliminary retrieval of relevant context documents before generating the response. By having the necessary information available in advance through the RAG module, the system prevents hallucinations that would occur if the model tried to generate responses without sufficient contextual grounding.
3Adaptability or versatility
If conventional systems lack integration with other critical systems, then the system structure is simple, but the system cannot provide holistic support
Solution Approach 1:
The patent designs a modular architecture where the embedding model, RAG module, and verification stage can serve multiple functions across different domains. The same core components handle various types of queries and can integrate with different data sources, providing holistic support without requiring domain-specific customization of the entire system.
4Adaptability or versatility
If conventional systems lack adaptability in environments where rules or data frequently change, then the system is stable, but the system cannot comprehensively adapt to changing conditions
Solution Approach 1:
The patent implements dynamic retrieval where the system adapts to changing data by retrieving relevant context based on the specific query and current state of the knowledge base. The embedding models and retrieval mechanisms are designed to work with evolving data without requiring complete system reconfiguration, maintaining stability while enabling adaptability.
Data Source
AI summary
Techniques for automated query response determination using a hybrid AI are disclosed herein. An example computer-implemented method includes receiving a data file and a request including at least one query associated with the data file and applying a hybrid model to the data file. Applying the hybrid model includes segmenting the data file into one or more portions, embedding the one or more portions into a vector space, extracting, from the data file, data associated with one or more classifications, storing (i) the embedded portions in a first database and (ii) the extracted data in a second database, and determining a response to request queries based on the embedded portions and the extracted data, wherein the hybrid model constrains each response based on a parametric input prompt associated with the extracted data. The example computer-implemented method further includes storing one or more data objects indicating each response.