In-Database ML Prompt Generation for Context-Aware LLMs
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Solution Overview
Problem
Current generative AI models lack proactivity and require significant user interaction, leading to inefficiencies in prompt generation and accuracy due to overgeneralized prompts and the 'lost in the middle' phenomenon, especially when handling dynamic and context-specific tasks.
Innovation Solution
Integrate in-database machine learning models with large language models (LLMs) to predict relevant context, using automatic triggers and learned summarization, and leverage vector stores for similarity searches to enhance prompt generation and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If overgeneralized prompts are used to accommodate limited variation during reuse, then the prompt can be reused across different use cases, but semantic accuracy and task accuracy degrade due to the 'lost in the middle' phenomenon and inability to handle context-specific tasks
Solution Approach 1:
The patent segments the prompt into static portions (which can be reused) and dynamic portions (which are customized using ML predictions for each specific task). This allows the prompt template to be reused across different use cases while injecting context-specific information through machine learning predictions, thereby maintaining both reusability and semantic accuracy.
2Adaptability or versatility
If all potential context information is included in the prompt to handle all use cases, then the prompt becomes more comprehensive, but model performance degrades due to the 'lost in the middle' phenomenon where relevant information gets buried
Solution Approach 1:
The system performs preliminary action by using machine learning models to predict and pre-filter the most relevant context information before it is passed to the LLM. This proactive filtering ensures that only the most pertinent context is included in the prompt, preventing information overload and avoiding the 'lost in the middle' phenomenon while maintaining comprehensive context coverage.
3Ease of operation
If generic prompts are used for all use cases, then the system is simpler to operate, but the system requires significant user interaction and manual prompt handcrafting to achieve acceptable accuracy
Solution Approach 1:
The system implements self-service by automatically generating context-specific prompts using machine learning models that analyze user data and predict relevant information. This eliminates the need for manual prompt handcrafting and reduces user interaction overhead, as the system autonomously adapts prompts to specific tasks while maintaining simplicity of operation.
4Measurement precision
If manual prompt handcrafting is performed for specific tasks, then semantic accuracy and task accuracy improve, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent introduces dynamics by using machine learning models to automatically adapt and generate prompts based on real-time analysis of user data and context. This dynamic approach replaces static manual prompt handcrafting with an automated system that adjusts prompts to specific tasks, maintaining high accuracy while reducing the complexity of manual intervention.
Data Source
AI summary
A database system integrates in-database machine learning (ML) models with in-database large language models (LLMs) or other generative artificial intelligence (AI) models that enable new applications. The database system receives one or more inferences from an ML model and provides an inference input to a retrieval agent of an object store. One or more vector stores represent a plurality of reference documents using semantic encodings. The retrieval agent performs a similarity search of the one or more vector stores to retrieve a set of passages from the plurality of reference documents based on similarity of encodings of the inference input and encodings of passages in the plurality of reference documents. The database system generates a linguistic prompt for an LLM having a context including the inferences and passages and applies the LLM to the linguistic prompt to generate a natural language explanation of the one or more inferences.