Chained Conversational Prompt Engineering for Document Extraction
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Solution Overview
Problem
Existing techniques struggle to efficiently extract accurate insights from large collections of unstructured and varied documents, due to challenges in data organization, table detection, and the limitations of standalone prompt-based language models.
Innovation Solution
A system that processes user queries by generating a dynamically configured workflow with states and tools, using a Large Language Model (LLM) to select states, provide execution sequences, and choose sub-agents for each state, along with a method of prompt engineering that chains conversational prompts to retrieve information from multiple documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional techniques are used to extract information from large document collections, then the process can be simple, but the extraction accuracy and efficiency deteriorate due to difficulty in handling unstructured and varied document formats
Solution Approach 1:
The system segments the information extraction process into distinct states (retrieve, load and convert, extract and analyze, document search, identify, validate, compile) that can be dynamically selected and executed in sequence. Each state handles specific aspects of document processing, allowing complex extraction tasks to be broken down into manageable components that improve accuracy without overwhelming system complexity
Solution Approach 2:
The system dynamically generates workflows by selecting and ordering states based on the specific query and document characteristics. This dynamic adaptation allows the system to optimize the extraction process for different document types and information needs, improving extraction accuracy while maintaining reasonable system complexity through on-demand configuration
2Measurement precision
If specialized models are trained for a given domain, then extraction accuracy improves, but the manual effort and time required deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-defining a library of extraction states and their associated tools before actual information extraction is needed. These pre-configured states cover common extraction scenarios and can be dynamically assembled for specific tasks, eliminating the need for time-consuming domain-specific model training while maintaining high extraction accuracy
Solution Approach 2:
The extraction states and tools are designed to be universal and applicable across multiple domains and document types. Rather than training specialized models for each domain, the same set of states can be dynamically configured to handle diverse extraction tasks, significantly reducing the time and effort required while maintaining accuracy through context-appropriate state selection
3Measurement precision
If manual analysis is performed on documents, then extraction accuracy can be maintained, but the process becomes time consuming and difficult to implement at scale
Solution Approach 1:
The system enables automated self-service information extraction by configuring states and tools that autonomously perform extraction tasks without manual intervention. The dynamic workflow generation and state execution mechanisms allow the system to handle large volumes of documents automatically, maintaining extraction accuracy while achieving high processing throughput suitable for scaling to millions of documents
Solution Approach 2:
The system incorporates validation states that provide feedback on extraction results, automatically checking for consistency and accuracy. This feedback mechanism ensures high extraction accuracy is maintained through automated validation rather than manual review, enabling the system to process documents at scale while preserving quality through iterative validation loops
4Device complexity
If standalone prompt-based language models are used, then the system remains simple, but the models become overwhelmed with too much information and fail to accurately retrieve data
Solution Approach 1:
The system segments the information processing load by retrieving only relevant documents and passages before passing them to the language model for extraction. The retrieve and document search states filter large document collections down to manageable subsets, preventing the language model from being overwhelmed while maintaining system simplicity through modular state execution
Solution Approach 2:
The system extracts and isolates only the necessary information from documents before presenting it to the language model. By using extraction states to pull out specific relevant passages and data points rather than feeding entire documents, the system maintains simplicity while improving retrieval accuracy through focused, context-appropriate input to the language model
5Loss of information
If large language models are employed for analyzing documents, then information retrieval capability improves, but the generation of inaccurate, low-quality, hallucinated, and/or fabricated information worsens
Solution Approach 1:
The system incorporates validation states that provide feedback loops to verify information extracted by the language model. These validation mechanisms check for consistency, cross-reference with source documents, and identify potential hallucinations or fabrications, thereby maintaining high information retrieval capability while significantly improving the reliability and accuracy of the extracted information
Solution Approach 2:
The system performs preliminary validation and cross-checking of language model outputs before finalizing extraction results. By预先 establishing validation criteria and cross-referencing mechanisms in the workflow configuration, the system maintains strong information retrieval capability while preventing inaccurate or fabricated information from being presented as facts
Data Source
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AI summary
A system is provided for processing user queries by using an automated agent and a workflow. The system comprises reusable components that include states, tools, and/or data sources. Based on analysis of a query's content and goals, the system generates a workflow comprising a sequence of states, each state optimized for a subtask and dynamically bound to a selected tool(s) for that specific query. The workflow can provide a structured high-level control, while allowing for flexible selection of the tool(s) for each state of the workflow for that given query. The system produces a result using the structured workflow and selected tools, answering a user's original query.