Embedded AI Attributes for Context-Aware Prompt Steering
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Solution Overview
Problem
Current approaches to steering generative AI behaviors are challenging due to the sensitivity of generative AI systems to input prompts, requiring users and developers to write lengthy custom prompts to capture context, which is inefficient and difficult to customize for specific content or applications.
Innovation Solution
An AI guidance system that generates or modifies input prompts based on embedded attributes associated with applications, documents, and interfaces, providing additional instructions to guide generative AI systems, allowing for finer control of AI behaviors on both client and server sides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users write lengthy custom prompts to capture context, then the precision of guiding generative AI behaviors is improved, but the time and effort required increases significantly
Solution Approach 1:
The patent applies preliminary action by embedding attributes (such as context, tone, style, and content guidelines) directly into documents, webpages, and applications during their creation. This allows the generative AI system to access pre-prepared contextual information without requiring users to write lengthy prompts each time, thus improving guidance precision while reducing repeated time investment.
Solution Approach 2:
The patent uses copying by extracting and reusing embedded attributes from source documents and applications. Instead of manually crafting prompts, the system copies relevant contextual attributes (like authoring style, tone, and content requirements) directly from the embedded metadata, significantly reducing the effort and time needed to create precise guiding prompts.
2Ease of operation
If users write lengthy custom prompts to capture context, then the control over generative AI outputs is improved, but the complexity of the operation increases
Solution Approach 1:
The patent applies self-service by enabling documents, webpages, and applications to automatically provide their own contextual attributes to the generative AI system. The embedded attributes (such as content guidelines, tone, and style) serve themselves as prompts, eliminating the need for users to manually craft and manage complex prompt structures, thus simplifying operation while maintaining control.
Solution Approach 2:
By pre-embedding control attributes into documents and applications during creation, the system allows users to exercise control over generative AI outputs without facing the complexity of prompt engineering. The preliminary embedding of guidelines, tone, and content requirements transforms complex control mechanisms into simple, automated processes.
3Ease of operation
If generic prompts are used without customization, then the ease of use is improved, but the adaptability to specific content and applications deteriorates
Solution Approach 1:
The patent applies local quality by embedding specific attributes (such as content guidelines, tone, style, and contextual information) directly into individual documents, webpages, and applications. This allows each document or application to have customized guiding attributes tailored to its specific needs, improving adaptability while maintaining ease of use because users simply interact with the embedded attributes automatically.
Solution Approach 2:
The patent achieves universality by creating a standardized embedding mechanism that can be applied across diverse document types, applications, and content formats. The same embedding technology works universally for webpages, documents, images, and various applications, providing both ease of use through consistent interaction and adaptability through content-specific embedded attributes.
Data Source
AI summary
Systems and methods for directing behavior of a generative artificial intelligence (AI) system are provided. In particular, a computing device may obtain an input prompt associated with a requested task for one or more generative artificial intelligence (AI) systems, obtain one or more attributes based on the input prompt, modify the input prompt based on the one or more embedded attributes, and provide the modified input prompt to the one or more generative AI systems.