Interactive Prompt Variation Generation With Heuristic Prompt Organization
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Solution Overview
Problem
Current manual approaches to reformulating prompts for AI systems are slow, error-prone, and lack standardization, leading to suboptimal outcomes and reduced productivity in user interactions.
Innovation Solution
A computer-implemented method that leverages machine learning techniques to automatically generate, organize, and update prompt variations based on user interactions, using a heuristic that aligns with user preferences and intent, enabling efficient and effective review of generated outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual approaches are used to reformulate prompts for AI systems, then users can customize prompts according to their needs, but the process becomes slow and error-prone
Solution Approach 1:
The system automatically generates prompt variations using machine learning algorithms, allowing the AI system to serve itself in creating optimized prompts without requiring manual intervention for each variation. This self-service approach resolves the contradiction by automating the process while maintaining quality.
Solution Approach 2:
The patent replaces manual mechanical prompt reformulation with an automated machine learning system. The mechanical process of manually editing and refining prompts is substituted with an intelligent system that generates multiple variations automatically, thereby increasing productivity while maintaining ease of use through systematic organization.
2Adaptability or versatility
If manual prompt reformulation is used, then users have control over prompt creation, but standardization is lacking leading to suboptimal outcomes
Solution Approach 1:
The system changes the parameters of prompt generation by using machine learning to systematically vary multiple parameters (wording, structure, tone, etc.) while maintaining core intent. This approach provides both adaptability through multiple variations and reliability through standardized evaluation and organization of these variations.
Solution Approach 2:
The patent creates a universal system that handles multiple prompt reformulation tasks through a single automated platform. This multi-functional system maintains adaptability by generating diverse variations while ensuring reliability through consistent application of machine learning algorithms and standardized organization methods across all prompt types.
3Productivity
If automated machine learning techniques are used to generate prompt variations, then productivity increases, but the complexity of organizing and managing variations increases
Solution Approach 1:
The patent segments the generated prompt variations into organized categories and groups them systematically. This segmentation approach manages the complexity by breaking down the large set of variations into manageable subsets, allowing users to navigate and select from organized groups rather than facing an overwhelming unstructured list.
Solution Approach 2:
The system introduces an intermediary organizational layer between the machine learning generation process and the user interface. This intermediary component automatically categorizes and structures the prompt variations, serving as a mediator that simplifies the user's interaction while maintaining the high productivity benefits of automated generation.
4Adaptability or versatility
If multiple prompt variations are generated automatically, then user experience is enhanced through more options, but the time required to review and select variations increases
Solution Approach 1:
The system generates a comprehensive set of prompt variations beyond what a single user might manually create, providing excessive options that ensure the best possible prompts are available. However, the organized presentation allows users to efficiently review only the most relevant variations, reducing the time cost while maintaining the benefit of having multiple high-quality options.
Solution Approach 2:
The patent performs preliminary organization and categorization of prompt variations before presenting them to users. This preliminary action of structuring the variations in advance reduces the time users need to spend reviewing them, as the work of sorting and organizing has already been completed by the system.
Data Source
AI summary
In an approach to improve prompt variations to enhance user-based experiences while interacting with artificial intelligent (AI) systems, embodiments generate variations of the prompts associated with the initial input by automatically leveraging machine learning techniques and collect the prompts from a user resulting from an interaction with an artificial intelligent (AI) system. Further, embodiments, identify a heuristic to organize suggestions and the variations of the prompts, and determine and utilize the heuristic to organize the variations of the prompts in systematic categories to produce an efficient display of the variations of the prompts, in a graphic user interface (GUI), for the user to view. Additionally, embodiments, update the variations of the prompts based on identified interaction data from the user, and dynamically output the updated variations of the prompts in response to the identified and collected user data.