LLM Tool-Orchestrated Prompting for Accurate Document Extraction
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Solution Overview
Problem
Existing systems struggle to efficiently and accurately extract information from documents using machine learning models, particularly in complex scenarios involving large volumes of data, without sufficient preparation and correction mechanisms.
Innovation Solution
A system utilizing machine learning models that receive prompts to identify tasks, select appropriate tools, and perform steps to generate accurate replies, incorporating document information, tool information, user history, and system prompts to enhance the extraction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models directly extract information from documents without preparation steps, then the process is simpler and faster, but the accuracy and reliability of extracted information deteriorates
Solution Approach 1:
The system performs preliminary actions by generating preparation prompts that instruct the machine learning model to execute preparatory steps before information extraction. These preparation steps include understanding document structure, identifying relevant sections, and setting up extraction frameworks, which improve extraction accuracy while managing complexity through structured preprocessing.
Solution Approach 2:
The patent introduces an intermediary preparation prompt mechanism that mediates between the raw document and the extraction process. This intermediary layer analyzes the document first, generates appropriate preparation instructions, and then guides the extraction process, thereby improving accuracy without directly increasing the complexity of the core extraction function.
2Reliability
If machine learning models perform comprehensive task preparation and use multiple tools, then the quality of extracted information improves, but the time and computational resources required increase
Solution Approach 1:
The system executes preliminary actions by generating preparation prompts that guide the model to perform necessary preparatory tasks before extraction. This includes analyzing document structure, identifying extraction targets, and configuring appropriate tools in advance, which improves reliability while minimizing time loss through efficient preprocessing rather than repeated corrections.
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates the sufficiency of preparation prompts and adjusts them iteratively. The model receives feedback on whether the preparation was adequate and refines its approach, improving extraction reliability over time while optimizing processing time through learned efficiency from previous interactions.
3Productivity
If the system provides detailed prompt information including document information, tool information, and user history, then the machine learning model's task performance improves, but the complexity of prompt generation and processing increases
Solution Approach 1:
The patent creates a universal prompt generation framework that handles multiple types of information (document information, tool information, user history) through a single integrated preparation prompt mechanism. This multi-functional approach improves task completion efficiency by providing comprehensive context while managing complexity through a unified processing structure rather than separate handling for each information type.
Solution Approach 2:
The system segments the comprehensive prompt information into distinct components (document information, tool information, user history) that are processed in a structured sequence. This segmentation allows the machine learning model to handle complex information systematically, improving productivity by ensuring all relevant context is provided while managing processing complexity through organized, modular prompt construction.
Data Source
AI summary
Systems and methods to use one or more machine learning models to perform tasks as prompted using one or more tools are disclosed. Exemplary implementations may receive user input indicating a query; generate prompt information defining a prompt based on the query; provide the prompt as input to one or more machine learning models; obtain one or more replies for individual tasks; and present the one or more replies. The prompt may be configured to prompt the one or more machine learning models to identify the individual tasks based on the prompt; determine a step to be completed; select a tool for performing the step; perform the step using the tool to generate a first tool result; generate an intermediary prompt based on the first tool result; determine whether the intermediary prompt is sufficient; identify tools for generating the one or more replies; and generate the one or more replies.