Chained Prompt Workflows with Sub-Agents for Accurate Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques struggle to efficiently extract accurate insights from large, unstructured datasets due to the varied document structures and the limitations of traditional models, which often require extensive manual effort and lack flexibility, leading to inaccurate and hallucinated information.
Innovation Solution
A system that processes user queries through a dynamically generated workflow of states, using a combination of large language models (LLMs) and sub-agents to perform specific tasks, including retrieval, conversion, and analysis, with tools tailored to the query's objectives, and employs prompt engineering to validate and compile results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional techniques are used to extract information from large datasets, then manual effort can be reduced, but accuracy and precision of extracted insights deteriorate due to varied document structures and model limitations
Solution Approach 1:
The system segments the information extraction process into multiple specialized sub-agents, each responsible for specific tasks such as table detection, text extraction, and data validation. This segmentation allows each component to focus on its specialized function, improving overall accuracy while maintaining high processing efficiency through parallel operation of multiple agents.
Solution Approach 2:
The system introduces an intermediary validation layer that cross-checks extracted information against multiple sources and applies consistency checks. This intermediary mechanism filters out hallucinated or inaccurate information while preserving valid insights, thereby improving accuracy without significantly impacting extraction speed.
2Measurement precision
If specialized models are trained for a given domain, then extraction accuracy can be improved, but flexibility to account for minor changes deteriorates due to extensive manual retraining effort required
Solution Approach 1:
The system employs dynamic prompt engineering where extraction instructions and parameters are adjusted in real-time based on the detected document type and domain characteristics. This dynamic adaptation allows the system to maintain high accuracy across different domains without requiring retraining, as the prompts are automatically customized to match the specific domain requirements.
Solution Approach 2:
The system changes operational parameters such as extraction depth, validation thresholds, and processing strategies based on the detected document characteristics. By dynamically adjusting these parameters rather than retraining models, the system adapts to minor domain changes efficiently while maintaining specialized extraction accuracy for each domain.
3Ease of operation
If standalone prompt-based language models are used, then flexibility and ease of operation are improved, but information retrieval accuracy deteriorates due to model overload from too much information
Solution Approach 1:
The system segments the input information into manageable chunks and processes them through multiple specialized sub-agents rather than overwhelming a single model. Each sub-agent handles a specific aspect of information extraction, preventing overload and maintaining high accuracy while preserving the flexibility of prompt-based operation.
Solution Approach 2:
The system applies partial processing where only the most relevant portions of documents are extracted and analyzed in detail, while less critical information receives lighter processing. This selective approach prevents model overload by focusing computational resources on high-value information while maintaining system flexibility.
4Measurement precision
If manual analysis is performed, then extraction precision can be improved, but productivity and scalability deteriorate due to time-consuming processes that are difficult to implement at scale
Solution Approach 1:
The system implements self-service mechanisms where sub-agents automatically validate and cross-check each other's extracted information without human intervention. This automated mutual validation maintains high extraction accuracy comparable to manual analysis while enabling scalable processing of large document volumes through parallel operation of multiple self-validating agents.
Data Source
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.