LLM-Based Conversational Search Query Generation
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Solution Overview
Problem
Users without expertise in data storage systems face challenges in performing effective searches due to the need for specific search terms, leading to irrelevant results and poor user experience.
Innovation Solution
A system that processes natural language queries by determining contextual information and using a machine learning-based language model to generate specific search queries, enhancing search results by adding relevant keywords and attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search systems require specific search terms and expertise, then search precision may be improved, but ease of operation deteriorates and user accessibility worsens
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the search system. This intermediary automatically generates relevant search terms and queries from casual user input, eliminating the need for users to possess search expertise while maintaining search precision through automated term generation and expansion
2Ease of operation
If the system accepts broad unstructured queries, then ease of operation improves, but measurement precision deteriorates and irrelevant results increase
Solution Approach 1:
The system performs preliminary processing of broad queries by automatically generating multiple potential search terms, expanding query concepts, and pre-filtering relevant attributes before executing the search. This preliminary action ensures that even broad unstructured queries are transformed into precise search operations that return relevant results
3Measurement precision
If the system returns detailed and specific search results, then measurement precision improves, but loss of information increases when user intent is not properly captured
Solution Approach 1:
The system incorporates feedback mechanisms that analyze user interactions, query patterns, and result engagement to continuously refine its understanding of user intent. This feedback loop ensures that detailed search results are consistently aligned with actual user needs, preventing information loss by adapting to captured user intent over time
Data Source
AI summary
A system, for example, an online system uses a machine learning based language model, for example, a large language model (LLM) to process high-level natural language queries received from users. The system receives a natural language query from a user of a client device. The system determines contextual information associated with the query. Based on this information, the system generates a prompt for the machine learning based language model. The system receives a response from the machine learning based language model. The system uses the response to generate a search query for a database. The system obtains results returned by the database in response to the search query and provides them to the user. The system allows users to specify high level natural language queries to obtain relevant search results, thereby improving the overall user experience.