AI Content Generation Interface for Selective Output Combination
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Solution Overview
Problem
Conventional methods for customizing AI-generated content are inefficient, as they require iterative revisions of prompts, leading to high API call counts and difficulties in tracking historical outputs and preferred content data, while also failing to ensure coherence and responsiveness to initial prompts.
Innovation Solution
A system that allows users to selectively combine portions from multiple outputs of a generative model by presenting them via a user interface, enabling users to select and edit desired portions, which are then fed back into the model with an adherence weight to generate a final coherent output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If iterative revisions of prompts are used to customize AI-generated content, then content customization capability is improved, but API call count increases and efficiency decreases
Solution Approach 1:
The patent segments the content customization process into distinct phases: initial generation phase (single API call with base prompt) and refinement phase (user selects desired portions from multiple outputs). This segmentation eliminates the need for iterative API calls while maintaining customization capability, as users can combine portions from multiple initial outputs without requiring sequential revisions.
Solution Approach 2:
The system performs preliminary action by generating multiple content outputs simultaneously from a single prompt before user selection. This preliminary batch generation allows users to compare and combine outputs without triggering additional API calls, thereby improving efficiency while preserving adaptability.
2Adaptability or versatility
If iterative prompt revisions are performed, then content customization is improved, but tracking historical outputs and preferred content data becomes difficult
Solution Approach 1:
The patent implements feedback mechanisms that automatically track and store user preferences, selected portions, and output history in a structured database. This feedback system maintains a comprehensive record of all generations and user interactions, enabling the system to recall and reference historical data without loss, thereby supporting both customization and information tracking.
3Adaptability or versatility
If multiple API calls are made for iterative revisions, then content customization capability is improved, but coherence and responsiveness to initial prompts may be compromised
Solution Approach 1:
The system performs preliminary batch generation of multiple content outputs from a single well-crafted prompt that establishes coherence and responsiveness to the initial intent. By generating all candidate outputs upfront with the original prompt context, the system ensures that all outputs maintain coherence with the initial request, and user selection from these pre-generated outputs preserves this coherence without requiring additional API calls that might drift from the original intent.
Data Source
AI summary
A computer-implemented method is disclosed. The method includes: obtaining at least one output of a generative model based on input of a first text prompt; presenting the at least one output via a user interface; receiving, via the user interface, user selection of a desired portion of the at least one output; modifying the first text prompt based on the user selection to obtain a second text prompt; and providing the second text prompt as input to the generative model for obtaining a second output.