Query Compiler Feedback for LLM Database Query Errors
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Solution Overview
Problem
Machine learning models (MLMs), particularly large language models (LLMs), generate database queries with inaccuracies due to randomness, dialect inconsistencies, incorrect predictions, and lack of sufficient context, leading to uncorrectable errors.
Innovation Solution
Implement a query compiler that analyzes MLM-generated queries, corrects correctable errors, and generates prompts for MLMs to address uncorrectable errors by providing context and instructions for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models generate database queries autonomously, then query generation speed and automation are improved, but query accuracy and reliability deteriorate due to uncorrectable errors
Solution Approach 1:
The patent introduces a query compiler as an intermediary component between the machine learning model and the database execution system. The compiler analyzes generated queries, identifies uncorrectable errors, and provides targeted feedback to the MLM for correction, thereby maintaining high automation while improving query accuracy through intermediate validation and feedback mechanisms
Solution Approach 2:
The system implements a feedback loop where the query compiler analyzes MLM-generated queries, identifies errors that cannot be automatically corrected, and generates prompt elements with specific guidance back to the MLM. This feedback mechanism enables the MLM to learn from its mistakes and improve query generation accuracy over time while maintaining autonomous operation
2Loss of time
If the query compiler attempts to correct all errors automatically, then processing time is reduced, but manufacturing precision deteriorates because some errors require MLM intervention
Solution Approach 1:
The error correction process is segmented into two distinct phases: first, the query compiler automatically corrects errors that can be identified and fixed through compilation analysis; second, for uncorrectable errors, the system generates targeted prompt elements and requests MLM intervention. This segmentation allows efficient handling of correctable errors while preserving accuracy for complex errors requiring semantic understanding
Solution Approach 2:
The system performs partial automatic correction by handling only those errors that fall within the compiler's correction capabilities, rather than attempting to correct all errors automatically. This partial action approach optimizes processing time for straightforward cases while maintaining high accuracy by escalating complex errors to the MLM for specialized handling
Data Source
AI summary
A method for using a compiler to modify prompts for MLMs used to generate database queries includes receiving, at a query compiler, a first query of a database. The first query is at least in part generated by an MLM. The method includes determining, by the query compiler, whether the first query comprises an uncorrectable error. The method includes, responsive to determining that the first query comprises an uncorrectable error, generating a prompt element that describes the uncorrectable error and that is structured for inclusion in a prompt requesting the MLM to generate a modified first query that corrects the uncorrectable error. The method may include generating the prompt for the MLM, providing the prompt to the MLM, and responsive to providing the prompt, receiving, at the database query compiler, the modified first query that corrects the uncorrectable error.