Persona-Tailored Generative AI Content from Structured Data
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Solution Overview
Problem
Existing data mining technologies consume excessive computing resources and fail to generate content in the desired tone and voice for a specific target audience, leading to inefficient and repetitive user interactions, increased network latency, and storage device I/O operations.
Innovation Solution
Leveraging generative AI to generate content corresponding to a persona by receiving context from a user, including a persona and prompt, and utilizing a generative AI model to derive content from a dataset, reducing the need for repetitive user interactions and optimizing computing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing data mining technologies are used to transform data into meaningful information, then data can be processed and presented, but excessive computing resources are consumed and the content is not tailored to the desired tone and voice for the target audience
Solution Approach 1:
The system performs preliminary actions by pre-processing data into structured formats and pre-defining persona characteristics before actual content generation. This allows the generative AI model to efficiently retrieve and adapt pre-processed information rather than processing raw data from scratch, reducing computing resource consumption while maintaining content adaptability to specific personas
Solution Approach 2:
The system introduces an intermediary layer between data storage and content generation that includes a persona definition component and a data transformation component. This intermediary layer pre-structures data and defines persona characteristics, enabling the generative AI model to efficiently generate persona-tailored content without directly processing raw data, thus reducing computing resource consumption
2Productivity
If existing data mining technologies are used, then data can be transformed into information, but repetitive user interactions are required and network latency increases
Solution Approach 1:
The system performs preliminary data transformation and structuring before content generation requests. By pre-processing data into searchable, structured formats and pre-defining persona characteristics, the system enables single-interaction content generation that is tailored to the desired persona, eliminating repetitive user interactions and reducing network latency
Solution Approach 2:
The system implements self-service by automatically transforming raw data into meaningful information and generating persona-tailored content without requiring repetitive user interactions. The generative AI model autonomously retrieves pre-processed data, applies persona characteristics, and generates appropriate content in a single interaction, significantly reducing time loss and improving productivity
3Ease of operation
If existing data mining technologies are used, then data processing can be performed, but storage device I/O operations increase
Solution Approach 1:
The system performs preliminary data structuring and organization before content generation. By pre-processing data into optimized storage formats and pre-defining persona characteristics in accessible structures, the system minimizes storage device I/O operations during actual content generation, enabling ease of operation through single-interaction queries while reducing energy loss from repeated I/O operations
Data Source
AI summary
Generative artificial intelligence (AI) is leveraged to generate content corresponding to a persona. Context comprising a persona and a prompt is received from a user. The context is provided to a generative AI model. Based on the context, content at least partially derived from one or more files in a dataset is received from the generative AI model. The content corresponding to the context is provided to the user.