Intermediate Query Generation for Transparent LLM Data Retrieval
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Solution Overview
Problem
Existing real estate agent tools face inefficiencies and inaccuracies in handling repeated queries for the same information and complex requests due to limited visibility into the model's inference process, leading to significant computational resource costs and time wastage.
Innovation Solution
An agent assistant system that utilizes a large language model (LLM) to generate intermediate database queries, providing a natural language summary of the query process and allowing user validation, thereby enhancing accuracy and reducing inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large language model is used to process natural language queries, then the ability to understand and respond to complex requests is improved, but computational resource costs and processing time increase significantly
Solution Approach 1:
The patent segments the query processing into multiple stages: first generating an intermediate SQL query from the natural language input, then executing the SQL query to retrieve data, and finally generating the natural language response. This segmentation allows the system to use the LLM only for language understanding and response generation, while delegating data retrieval to traditional database systems, thereby reducing overall computational costs.
Solution Approach 2:
The patent introduces an intermediate SQL query as a mediator between the natural language input and the database. This intermediate representation serves as a bridge that translates user intent into structured database operations, reducing the computational burden on the LLM by offloading the data retrieval task to the database engine.
2Reliability
If intermediate operations are made transparent for user validation, then accuracy and trustworthiness improve, but system complexity and processing time increase
Solution Approach 1:
The patent implements a feedback mechanism where the generated intermediate SQL query is presented to the user for validation before execution. The user can confirm, modify, or reject the query, providing feedback that ensures the intermediate representation accurately reflects the user's intent. This feedback loop enhances reliability while maintaining manageable system complexity through a straightforward approval workflow.
3Speed
If the model processes each query independently without visibility into intermediate steps, then processing speed is maintained, but accuracy and ability to handle repeated queries deteriorate
Solution Approach 1:
The patent performs preliminary action by generating and validating the intermediate SQL query before actual data retrieval. This preliminary step ensures that the query structure is correct and aligns with user intent, improving accuracy. The pre-generated query can also be cached and reused for repeated queries, maintaining processing speed while enhancing accuracy.
Data Source
AI summary
Systems and methods for an agent assistant system to provide answers and insights to property-based questions in a natural language processing environment. Specifically, the agent assistant system as described herein may receive a question or task from a user and generate a query representing the request for information from the user and any other relevant information related to the request. Queries generated by the query generation system may represent an intent of the user-provided question and allow visibility into the request that a model is tasked with handling. In some embodiments, the agent assistant system may generate a natural language summary that represents the information parsed from the user and the task to be executed by the LLM. The summary may serve as a check or validation to confirm that the LLM will generate an accurate response based on the user input.