Generative Workflow Objects for Repeatable Document Regeneration
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Solution Overview
Problem
Existing generative AI systems struggle with maintaining consistency and efficiency in content generation across revisions of structured documents, as changes in input data or document edits require users to repeatedly regenerate content, leading to inefficiencies.
Innovation Solution
A generative workflow object is embedded within a structured document to maintain contextual information, enabling automated and consistent content regeneration by storing input content, transformation parameters, and historical interactions with AI models, allowing for efficient and accurate content generation and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual content regeneration is performed after document edits, then content consistency can be maintained, but user productivity decreases due to repeated manual intervention
Solution Approach 1:
The system enables self-service by automatically detecting document changes and triggering content regeneration without user intervention. The workflow object monitors source regions, identifies edits, and autonomously regenerates affected destination content, eliminating the need for users to manually regenerate content while maintaining consistency.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring document changes and using this information to trigger appropriate regeneration actions. The workflow object tracks modifications in source regions and feeds this information back into the regeneration process, ensuring content consistency is maintained through automated response to changes.
2Productivity
If automated content regeneration is implemented, then user productivity improves, but system complexity increases due to workflow management requirements
Solution Approach 1:
The system reduces complexity through segmentation by dividing the document into distinct source and destination regions with explicit relationships. The workflow object is segmented into discrete components including change detectors, regeneration triggers, and validation mechanisms, making the automated system easier to manage and understand.
Solution Approach 2:
The system applies preliminary action by pre-defining workflow objects with source and destination region relationships before document editing occurs. This upfront configuration establishes regeneration rules and dependencies in advance, reducing the complexity of real-time decision-making during automated regeneration processes.
3Measurement precision
If contextual information is stored in workflow objects, then regeneration accuracy improves, but information storage requirements increase
Solution Approach 1:
The system extracts only the essential contextual information needed for regeneration into workflow objects, such as source region identifiers, destination region identifiers, and relationship definitions. By taking out only the critical data elements rather than storing complete document contexts, the system maintains high regeneration accuracy while minimizing storage requirements.
Data Source
AI summary
Machines, media, and processes to form and embed one or more generative workflow objects in a structured document. A generative workflow object is associated with input content associated with a source region of the structured document and an output content associated with a destination region of the structured document. A prompt is generated based on the generative workflow object and provided to an artificial intelligence (AI) model to generate the output content based on the input content. The generative workflow object is updated based on interactions with the AI model and used to generate future prompts to enable repeatable regeneration of the output content in response to changes to content in the source region and/or the destination region.