LLM Workflow Generation System for Automated Task Structuring
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Solution Overview
Problem
Generating detailed workflows for complex tasks is challenging, as users may not know all the proper steps or be skilled enough to use appropriate frameworks to list workpieces with the necessary detail.
Innovation Solution
A system utilizing a large language model (LLM) artificial intelligence engine to automatically generate workflows from natural language instructions, with the option to use a document detailing the task instead of relying solely on the LLM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually create workflows following detailed instructions, then workflow accuracy and completeness are improved, but user skill requirements and time investment increase
Solution Approach 1:
The LLM-based system performs self-service by automatically generating workflows without requiring users to manually follow detailed instructions. The system takes natural language descriptions of tasks and autonomously translates them into structured workflows with appropriate workpieces and steps, eliminating the need for users to possess specialized knowledge of workflow frameworks.
Solution Approach 2:
The patent replaces the mechanical process of manual workflow creation with an automated LLM-based system. Instead of users mechanically following detailed instructions and framework requirements, the system uses natural language processing and LLM capabilities to generate workflows automatically, substituting human manual operations with AI-driven automation.
2Reliability
If users manually create workflows with detailed steps, then workflow completeness is improved, but time investment and complexity increase
Solution Approach 1:
The system performs self-service by automatically generating complete workflows including all necessary steps, workpieces, and dependencies. The LLM analyzes the natural language description and autonomously determines the complete workflow structure, eliminating the need for users to invest time in manually creating detailed step-by-step instructions.
Solution Approach 2:
The system performs preliminary action by pre-generating the complete workflow structure before the user needs to execute it. The LLM proactively creates the full workflow including all steps and workpieces based on the initial natural language description, so that when the user views or executes the workflow, it is already complete and ready to use.
3Reliability
If users manually create workflows using appropriate frameworks, then workflow professionalism is improved, but user expertise requirements increase
Solution Approach 1:
The LLM-based system performs self-service by automatically generating professional workflows using appropriate frameworks and terminology. The system understands the required professionalism and expertise levels and autonomously produces workflows that meet these standards without requiring users to possess such expertise themselves.
Solution Approach 2:
The LLM acts as an intermediary between the user's simple natural language description and the complex professional workflow framework. The system translates user-friendly descriptions into professionally structured workflows, mediating between the user's lack of expertise and the requirements for professional workflow creation.
Data Source
AI summary
Techniques are provided for workflow generation, including: (a) receiving, from a user, an instruction to create a workflow to perform a task; (b) generating one or more prompts based on the instruction and a predefined workflow schema; (c) querying a large language model (LLM) with the one or more prompts; (d) in response to querying the LLM, receiving a workflow in a format consistent with the predefined workflow schema from the LLM; and (e) displaying a visualization of the received workflow to the user.