Network-Guided AI Prompting for Deployment-Aware Digital Components
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Solution Overview
Problem
Current generative artificial intelligence models fail to consider the deployment environment and user interaction aspects when generating digital content, leading to suboptimal performance.
Innovation Solution
An AI system generates prompts based on network properties and user interaction data to optimize digital components for specific deployment environments, using trained models to refine and select components that meet performance criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current generative AI models are used to generate digital content, then content creation is achieved, but the content does not optimize for deployment environment and user interaction
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate digital components before deployment, evaluating their performance metrics using trained models, and selecting the optimal component in advance. This prevents suboptimal content from being deployed and ensures the selected component is tailored for the specific deployment environment and user interaction context.
Solution Approach 2:
The system implements feedback mechanisms by using trained evaluation models that assess digital components based on performance metrics derived from deployment environment properties and user interaction data. The feedback loop allows the system to iteratively improve component selection and generation, ensuring optimal performance in the target environment.
2Productivity
If digital components are generated without considering deployment context, then generation speed is maintained, but performance and relevance are reduced
Solution Approach 1:
The system segments the content generation process into distinct stages: generating multiple candidate components, evaluating each against performance metrics using trained models, and selecting the optimal component. This segmentation allows parallel processing of candidate generation and evaluation, maintaining productivity while achieving precise performance optimization through systematic assessment of each candidate.
Solution Approach 2:
The system changes parameters by adjusting generation prompts and models based on deployment environment properties and performance metric requirements. This allows the generation process to be tuned for both efficiency and precision, producing components optimized for specific contexts without sacrificing overall productivity.
3Reliability
If multiple digital components are generated and evaluated, then optimal performance is achieved, but computational resources and time increase
Solution Approach 1:
The system applies partial action by generating and evaluating a limited set of candidate digital components rather than exhaustively searching all possibilities. By using trained evaluation models to quickly assess candidates and select the top performer, the system achieves sufficient performance optimization without the time cost of exhaustive evaluation, balancing reliability with time efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating digital content using artificial intelligence. In one aspect, a method includes receiving, by an AI system, a request including text input descriptive of features of a digital component and one or more properties of a digital network associated with displaying the digital component. The AI system generates one or more prompts for use by one or more models to generate one or more digital components. The one or more prompts are generated based on the text input and the one or more properties of the digital network. The AI system obtains, from the one or more models, a digital component generated by the one or more models based on at least one of the one or more prompts. The digital components are distributed to the digital network for rendering at one or more client devices.