Vector Embedding LLM Chatbot for Tax Support Accuracy
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Solution Overview
Problem
Customer support agents, particularly consumer-facing professionals like tax professionals, face challenges in providing quick and accurate responses to complex customer inquiries due to the need to consult extensive resources, which can be time-consuming and inefficient.
Innovation Solution
The system utilizes vector embeddings and large language models (LLMs) to facilitate real-time access to topic-specific information. It embeds extensive tax-related documents into a vector space, allowing for similarity analysis and generating contextually relevant responses to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer support agents manually consult extensive resources to answer complex inquiries, then answer accuracy is improved, but response time increases and productivity decreases
Solution Approach 1:
The patent introduces an intermediary system comprising vector embedding models and large language models that act as a mediator between customer inquiries and the knowledge base. This intermediary automatically processes queries, retrieves relevant information, and generates accurate responses, eliminating the need for agents to manually search through extensive resources while maintaining high answer accuracy.
Solution Approach 2:
The patent replaces the mechanical manual search and consultation process with an automated computational system. Vector embeddings transform textual information into mathematical representations, and LLMs process these representations to retrieve and generate responses automatically, substituting human manual effort with machine-based information processing.
2Loss of information
If customer support agents manually search through extensive documents and resources, then comprehensive information is obtained, but time consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and embedding extensive tax-related documents into vector space before they are needed. This allows the system to quickly retrieve relevant information through vector similarity search without requiring agents to manually search through documents during customer interactions, thus preserving information completeness while minimizing time consumption.
Solution Approach 2:
The patent creates vector embeddings as mathematical copies of textual information, storing the essential semantic meaning in a compressed format. These vector copies enable rapid retrieval and processing of information without requiring access to the original extensive documents, reducing time spent on resource consultation while maintaining information completeness.
3Measurement precision
If extensive tax-related documents are embedded into vector space using advanced techniques, then information retrieval accuracy is improved, but system complexity increases
Solution Approach 1:
The patent employs a universal LLM architecture that can handle multiple functions including vector embedding generation, similarity computation, and response generation. This multi-functional approach improves information retrieval accuracy while avoiding the need for multiple separate complex systems, thus managing overall system complexity.
4Productivity
If real-time access to topic-specific information is provided through automated systems, then productivity is improved, but system complexity increases
Solution Approach 1:
The patent segments the information processing system into distinct functional modules: vector embedding generation, similarity search, and LLM-based response generation. This segmentation allows each component to be optimized independently for real-time performance while managing overall system complexity through modular architecture.
Data Source
AI summary
Systems and methods are provided for using vector embeddings and large language models to answer chatbot inquiries.