Schema-Aware ML Prompt Augmentation for SQL Query Engines
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Solution Overview
Problem
Existing large language models (LLMs) face challenges in handling multiple use cases due to the 'lost in the middle' phenomenon, where performance degrades when relevant information is not at the beginning or end of the context, leading to low semantic and task accuracy.
Innovation Solution
The integration of ML automation within a database system to dynamically engineer and augment linguistic prompts using metadata and vector store information, narrowing the search space to relevant database information, and leveraging ML models for prompt engineering to enhance LLM inferencing accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If overgeneralized prompts are used to accommodate multiple use cases, then adaptability is improved, but model performance degrades due to the 'lost in the middle' phenomenon
Solution Approach 1:
The patent segments the prompt into modular components: a static template portion and dynamic parameter portions. This allows the prompt to be reused across different use cases by simply changing parameters, while keeping the relevant information concentrated and accessible, thus avoiding the 'lost in the middle' phenomenon.
Solution Approach 2:
The patent introduces a new dimension of prompt organization by using structured templates with designated parameter slots. This transforms the flat, monolithic prompt structure into a multi-dimensional structure where static and dynamic elements are clearly separated, improving both reusability and performance.
2Reliability
If handcrafted prompts are used for specific tasks, then task accuracy is improved, but adaptability deteriorates as prompts cannot be reused for different use cases
Solution Approach 1:
The patent creates universal prompt templates that can serve multiple functions across different use cases. The template structure is designed to be adaptable to various tasks by changing the parameter values, making a single prompt design serve multiple purposes while maintaining task-specific accuracy.
Solution Approach 2:
The patent utilizes parameter changes as the key mechanism for adapting prompts to different use cases. By maintaining a consistent template structure and only changing the parameter values (such as topic, entity types, query specifics), the system achieves both high task accuracy and broad adaptability.
3Adaptability or versatility
If long context is provided to accommodate multiple use cases, then adaptability is improved, but model performance degrades due to information being lost in the middle
Solution Approach 1:
The patent extracts only the essential and relevant information needed for each specific task from the broader context. By using template parameters to specify exactly what information is needed (e.g., specific entity types, time ranges, locations), the system avoids including irrelevant information in the middle of the context that would be lost.
Solution Approach 2:
The patent performs preliminary action by pre-defining the prompt template structure with clearly marked parameter slots before actual use. This preparation ensures that when the prompt is instantiated with specific parameters, the relevant information is automatically positioned in optimal locations, avoiding the 'lost in the middle' problem.
Data Source
AI summary
A database system generates a prompt for an LLM or other machine learning (ML) model to narrow the search space to highly relevant information about a database. A distinct instance of a classifier, a clustering algorithm, or a topic modeling model can be trained based on information from ML automation within the database system, respectively for each column or table in the database. Model instances can then be used during generative LLM inferencing to identify relevant sources of data to answer the user's question. Thus, the prompt generation combines ML automation and other ML models or an LLM for topic modeling and schema description.