LLM Virtual Assistant Self-Improving Knowledge Base
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing sales and customer service interactions face challenges in providing accurate and timely product information, leading to potential sales losses due to incomplete or incorrect information.
Innovation Solution
A computer-implemented method using a large language model (LLM) trained with product information from a product knowledge base, which interacts with users through a synthetic human interface, allowing for self-improvement of product knowledge by evaluating user interactions and updating information based on quality metrics and information gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If product information is manually updated in the knowledge base, then information accuracy can be maintained, but time consumption and operational complexity increase
Solution Approach 1:
The system enables self-service by automatically updating the product knowledge base using LLM-generated content from user interactions. The knowledge base updates itself without manual intervention, extracting product information from conversations and autonomously maintaining accuracy while reducing time consumption.
Solution Approach 2:
The system implements feedback loops where user interactions with the virtual assistant are continuously analyzed. The LLM evaluates conversation quality and extracts product information, which feeds back into the knowledge base. This closed-loop feedback mechanism ensures information accuracy is maintained through automated learning from real user queries.
2Loss of information
If a comprehensive product knowledge base is maintained, then information completeness improves, but system complexity and maintenance burden increase
Solution Approach 1:
The knowledge base performs self-service by automatically extracting and adding product information from user interactions. The LLM identifies information gaps through quality metrics and autonomously populates the knowledge base with relevant product details, maintaining completeness without increasing management complexity.
Solution Approach 2:
The system performs preliminary actions by proactively identifying information gaps before they become problems. The LLM continuously evaluates conversation quality and preemptively extracts missing product information, ensuring the knowledge base remains comprehensive without requiring reactive manual updates.
3Measurement precision
If user interactions are thoroughly evaluated, then product knowledge quality improves, but processing time and computational resources increase
Solution Approach 1:
The system applies partial evaluation by focusing on specific quality metrics relevant to product knowledge extraction rather than thoroughly analyzing every aspect of user interactions. The LLM selectively evaluates conversations based on identified information gaps, achieving sufficient quality improvement without excessive processing overhead.
Solution Approach 2:
The system changes evaluation parameters by using quality metrics that prioritize product knowledge relevance over comprehensive conversation analysis. The LLM adjusts evaluation depth based on information gap identification, optimizing the balance between knowledge quality improvement and processing efficiency.
Data Source
AI summary
Techniques for managing artificial intelligence interactions are disclosed. A large language model (LLM) is trained, including information related to products for sale. The product information resides in a product knowledge base. Users interacting with a synthetic human generate input related to the products for sale through an embedded interface included in a website or application. The user input is captured and the LLM creates responses based on information in the product knowledge base. The responses are used to produce video segments that are presented to the user. The user creates additional input based on the video responses from the LLM. The additional input is evaluated and used to trigger self-improving steps to improve the content of the product knowledge base and the responses generated for the user. The self improving includes quality metrics; self-learning instructions; information gap identification; and information collection from third-party websites, product experts, and sellers.