LLM Prompt Generation for Image Scenario Creation
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Solution Overview
Problem
The inefficiency and cost-effectiveness of using generative machine learning models for creating content items, as they require time-consuming and expensive processes for generating prompts and selecting inputs.
Innovation Solution
The system intelligently and automatically selects content items of interest to users and processes them using generative ML models or large language models to enhance visual aspects, while dynamically generating context-sensitive prompts for personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If generative machine learning models are used to create content items, then content quality and uniqueness are improved, but time consumption and cost increase
Solution Approach 1:
The system pre-generates multiple potential content items using the generative ML model before the user needs them. These pre-generated items are stored and can be quickly retrieved and presented to the user, eliminating the need for on-demand generation and significantly reducing time consumption while maintaining high content quality
Solution Approach 2:
The system generates more content items than the user immediately needs (excessive action), creating a pool of pre-generated content. This allows the user to receive high-quality content quickly without requiring the system to generate exactly what is needed at the moment, thus reducing time consumption while maintaining content quality
2Manufacturing precision
If generative machine learning models are used to create content items, then content quality and uniqueness are improved, but cost increases
Solution Approach 1:
The system performs content generation in advance during periods when computational resources are more efficiently utilized, storing the generated content for later use. This batch processing approach reduces the overall computational cost compared to on-demand generation while maintaining high content quality
Solution Approach 2:
The system creates multiple variations of content items through the generative ML model, storing these copies in a library. When the user needs content, the system retrieves and presents from the pre-generated copies rather than generating new content each time, significantly reducing computational cost while maintaining content quality and uniqueness through variety
3Productivity
If automatic prompt generation is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system automatically generates its own prompts using the generative ML model without requiring manual intervention. The model autonomously creates content items based on predefined parameters and user preferences, improving productivity while the automated nature of the process manages complexity through self-service operations
Data Source
AI summary
Methods and systems are disclosed for suggesting scenarios for an image using one or more machine learning models based on an output of an LLM. The methods and systems generate, by a device of a user, a first prompt comprising a demographic of a person and a date, and process the first prompt by a large language model (LLM) to generate a plurality of ideas relevant to the person on that date, each idea comprising a respective description and vibe. The methods and systems generate a second prompt comprising a selected idea from the plurality of ideas and a request for a plurality of scenarios that are relevant to the selected idea, process the second prompt by the LLM to generate the plurality of scenarios that are relevant to the selected idea, and present an individual content item corresponding to an individual scenario of the plurality of scenarios.