Dynamic LLM Skills Agents for Contextual Ecommerce Product Q&A
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing e-commerce platforms face challenges in efficiently addressing customer questions about products due to the high cost and inefficiency of manually creating FAQs, and the difficulty in predicting the most relevant questions to display, leading to a cluttered user experience.
Innovation Solution
Utilizing Large Language Models (LLMs) to generate dynamic and contextually relevant questions and answers, such as FAQs, product comparisons, summaries of customer reviews, and suggested co-purchases, by training on product data and customer behavior, and employing scoring mechanisms to optimize the display of these responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually creating FAQs is used, then accuracy of product information is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service by having the LLM automatically generate FAQs and product information without requiring manual intervention from human experts. The model processes product data independently and produces accurate responses autonomously, eliminating the time-consuming manual creation process while maintaining information accuracy.
Solution Approach 2:
The patent replaces the mechanical manual creation process with an automated LLM-based system. Instead of human writers manually drafting FAQs, the system uses computational models that process product data and generate information automatically, substituting human labor with an intelligent automated mechanism.
2Reliability
If manually creating FAQs is used, then quality of product information is improved, but operational complexity and cost increase
Solution Approach 1:
The system performs self-service by automatically processing product data through the LLM to generate FAQs without requiring complex manual operations. The model independently analyzes product information, generates relevant questions and answers, and outputs structured data, eliminating the need for complex human operational processes while maintaining high quality.
3Ease of operation
If predicting relevant questions is used, then user experience is improved, but difficulty in predicting accurately increases
Solution Approach 1:
The system incorporates feedback mechanisms where the LLM analyzes user interactions and adjusts its question generation accordingly. By continuously learning from user behavior patterns and feedback, the model improves its prediction accuracy over time, making it easier to provide relevant questions while reducing the difficulty of accurate prediction.
Solution Approach 2:
The patent replaces complex manual prediction mechanisms with an automated LLM system that uses natural language processing and machine learning. This substitution transforms the difficult prediction problem into an automated process that leverages computational intelligence to accurately anticipate user needs without requiring complex manual analysis.
4Quantity of substance
If displaying all possible questions is used, then completeness of information is improved, but user interface clutter increases
Solution Approach 1:
The system applies segmentation by dividing the complete set of possible questions into prioritized segments based on relevance and user interest. The LLM ranks and filters questions to display only the most pertinent ones first, organizing information into manageable segments that maintain completeness while preventing interface clutter through structured presentation.
Solution Approach 2:
The patent implements partial action by selectively displaying only the most relevant questions and answers rather than all possible information at once. The LLM generates a curated subset of high-value content that provides sufficient information for users without overwhelming the interface, using selective disclosure to balance completeness with usability.
Data Source
AI summary
Exemplary embodiments include systems and methods for generating dynamic searches and responses for a customer of an ecommerce store, the systems and methods comprising: a source database storing context information regarding products listed on the ecommerce store; a user interface element supported by the ecommerce store, configured to receive a prompt-query pair comprising a user query and a pre-configured prompt, and further configured to populate a contextual response to the prompt-query pair; a server coupled to the user device; and a large language model coupled to the source database and the server. The large language model is configured to: generate an initial set of prompt-query pairs using context information for products listed on the ecommerce store; receive the prompt-query pair; and generate a contextual response using the pre-configured prompt and the information regarding the product stored in the at least one source database.