AI Document Generation With Vector Retrieval for Verified Responses
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Solution Overview
Problem
Current AI systems, such as ChatGPT models, suffer from unreliable responses due to hallucinations, particularly in providing scientific, academic, and technical information, leading to potential inaccuracies and integrity issues in authorship.
Innovation Solution
Implementing a context-specific vector database with robust prompts, query refinement, and limitations to prescribed data sources, using a processor to refine queries and generate comprehensive answers with verified references, as exemplified by the Cure AI model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current AI models like ChatGPT are used to generate responses to queries, then the system can provide conversational interface and generate text responses, but the responses suffer from hallucinations and unreliable behavior that limits reliability
Solution Approach 1:
The patent introduces an intermediary retrieval system between the user query and the AI model response. The system retrieves relevant information from external sources (web pages, documents, databases) and provides it as context to the AI model, thereby mediating the information flow and reducing hallucinations. This is implemented through query transformation, information retrieval, and context injection mechanisms.
Solution Approach 2:
The system performs preliminary actions by transforming the user query into multiple query variations, retrieving relevant information in advance, and preparing context before the AI model generates the response. This includes generating embedded representations, searching vector databases, and assembling relevant passages before the final response generation, ensuring the model works with pre-verified information.
2Productivity
If AI models generate comprehensive answers from general knowledge, then the system can provide broad information, but the information may be inaccurate or hallucinated without verified references
Solution Approach 1:
The system implements feedback mechanisms by continuously verifying information through multiple retrieval attempts, cross-checking multiple query variations, and validating sources. The retrieval-augmented generation process provides feedback loops where the system retrieves information, generates responses, and can iteratively refine both the queries and the retrieved context to ensure accuracy.
Solution Approach 2:
The system changes parameters by transforming queries into multiple variations, adjusting search thresholds, and modifying retrieval parameters to optimize both productivity and precision. The system dynamically adjusts the number of retrieved passages, the similarity thresholds, and the query transformation strategies to balance comprehensive information generation with verified accuracy.
3Reliability
If the AI system uses external data sources to improve accuracy, then the reliability of responses increases, but the system complexity increases due to query transformation and vector database operations
Solution Approach 1:
The patent segments the AI system into distinct functional modules: query transformation module, embedding generation module, vector database search module, context assembly module, and response generation module. Each module performs a specific function, making the overall complex system manageable through clear separation of concerns. The query is segmented into multiple variations, and the retrieval process is segmented into multiple stages.
Solution Approach 2:
The system implements universal components that serve multiple functions. The query transformation module not only generates query variations but also creates embedded representations for vector storage. The vector database serves both as a storage mechanism and a retrieval system. The retrieved context serves both as verification material and as input for the AI model, reducing the need for separate dedicated components.
Data Source
AI summary
A method for generating an hallucination-free, retrospectively verified document on a specific subject begins with a processor receiving an original query related to the specific subject. The processor then apples a system query refiner and a user query to the original query to generate a refined query. Next, the processor generates an embedding of the refined query and applies the embedding to a vector database containing vector representations of data objects in a big data source to determine a similarity between the embedding and the vector representations. The processor then applies the refined query and the most similar vectors to a large language model, applies the large language model to the big data source, generates the verified document using data objects from the big data source, and returning the verified document and an identification for each data object.