Hierarchical Prompting for Consistent Structured Object Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for generating structured data objects face challenges in ensuring internal consistency and adherence to object formats, leading to inconsistencies and violations due to the prioritization of user requests over format rules, especially in machine learning-based approaches.
Innovation Solution
A machine learning-based system that enforces a hierarchical structure of instructions, where object format rules have the highest priority, followed by application-specific and user preferences, ensuring consistent and predictable generation of structured objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning-based object generation is used to improve flexibility and customization, then adaptability is improved, but manufacturing precision deteriorates due to format rule violations
Solution Approach 1:
The system performs preliminary actions by establishing a hierarchical rule structure before object generation occurs. Format rules are defined and prioritized in advance, creating a framework that guides the machine learning model to generate objects that adhere to required formats while still allowing customization flexibility.
Solution Approach 2:
The patent introduces an intermediary hierarchical rule structure that mediates between user customization requests and format requirements. This intermediary layer translates and reconciles conflicting requirements, allowing the system to balance adaptability with manufacturing precision by processing requests through multiple priority levels.
2Ease of operation
If user requests are prioritized over format rules to improve ease of operation, then ease of operation is improved, but reliability deteriorates due to inconsistent object generation
Solution Approach 1:
The system establishes the hierarchical rule structure and priority relationships before processing user requests. This preliminary configuration ensures that format rules and consistency requirements are already in place to guide object generation, preventing reliability issues while maintaining ease of operation.
Solution Approach 2:
The hierarchical rule structure acts as an intermediary that processes user requests through multiple layers of validation and prioritization. This intermediary mechanism ensures that user requests are fulfilled while simultaneously maintaining object consistency and reliability by enforcing format rules at appropriate hierarchical levels.
3Manufacturing precision
If hierarchical instruction structure is implemented to improve manufacturing precision, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the instruction structure into multiple hierarchical levels, each handling specific aspects of object generation. This segmentation allows the complex task of maintaining format adherence to be divided into manageable priority levels, improving manufacturing precision while organizing complexity in a structured manner.
Solution Approach 2:
The hierarchical rule structure adds a dimensional aspect to the instruction system by organizing rules across multiple priority levels rather than a single flat structure. This dimensional organization manages complexity by providing clear hierarchical relationships, making the system more tractable while improving format rule adherence.
Data Source
AI summary
A system may receive a request to generate an object. A system may generate, based on the request, a first portion of a prompt associated with a first layer of a hierarchy and a second portion of the prompt associated with a second layer of the hierarchy, where the second layer has a lower priority than the first layer. A system may generate the object based in part on providing the portions of the prompt as input to a machine learning model, where the object is formatted according to an object format and is internally consistent. To generate the object the machine learning model does not violate instructions associated with a layer of the hierarchy based on instructions associated with a layer of the hierarchy having a relatively lower priority.