Guided Prompt Creation via Foundation Model Integrations
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Solution Overview
Problem
Users face challenges in generating optimal content using foundation models due to the need for precise natural language prompts, which can be time-consuming and resource-intensive, especially for inexperienced users.
Innovation Solution
A guided prompt creation process is introduced, where a computing device provides a user interface with modifier key components that users can select to add focus or direction to their prompts, with the foundation model suggesting corresponding modifier values to refine the prompt.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually craft precise natural language prompts for foundation models, then content generation quality improves, but time consumption and processing resources increase
Solution Approach 1:
The system performs preliminary action by automatically generating and presenting multiple prompt options to the user before the user needs to submit the prompt to the foundation model. The prompt generation component creates several refined prompts based on the user's initial input, allowing the user to select from pre-generated options rather than crafting prompts manually, thus reducing time consumption while maintaining quality
Solution Approach 2:
The system implements feedback by presenting generated prompt options to the user for selection and by monitoring user interactions to refine future prompt generations. The user feedback mechanism allows users to select from generated prompts or request modifications, creating a feedback loop that improves prompt quality over time while reducing the iterative manual crafting process
2Manufacturing precision
If users engage in multi-turn conversational exchange with foundation models, then desired output is achieved, but processing resources and time increase
Solution Approach 1:
The system performs preliminary refinement of the user's prompt before submission to the foundation model. The prompt generation component automatically creates multiple refined versions of the prompt, effectively performing the initial rounds of conversation and refinement before the actual content generation begins, thus reducing the number of multi-turn exchanges needed
Solution Approach 2:
The system creates multiple copies or variations of the user's initial prompt input. The prompt generation component generates several different prompt formulations based on the same user intent, allowing the user to select the most effective version or have the system try multiple variations automatically, reducing the need for repeated conversational turns
3Productivity
If users use canned prompts to generate content, then content generation speed increases, but creativity and unexpected output decrease
Solution Approach 1:
The system dynamically generates prompts based on the user's specific input and context rather than using static canned prompts. The prompt generation component adapts its output based on the user's initial prompt, selected modifier keys, and modifier values, creating dynamic prompt variations that maintain creativity while improving generation speed through automated refinement
Solution Approach 2:
The system changes parameters of the prompt generation process by allowing users to select from multiple modifier keys and modifier values that can be dynamically combined with the user's input. This parameter variation mechanism enables the system to generate diverse, creative prompts automatically rather than relying on fixed canned prompts, thus maintaining versatility while improving efficiency
4Ease of operation
If inexperienced users interact with foundation models, then content generation is accessible, but prompt precision and output quality decrease
Solution Approach 1:
The system introduces an intermediary component - the prompt generation component - that acts as a mediator between the user's simple input and the foundation model's requirements. This intermediary automatically refines and expands the user's basic prompt into precise, well-structured prompts suitable for high-quality generation, shielding inexperienced users from the complexity of crafting effective prompts directly
Solution Approach 2:
The system enables self-service by automatically generating and presenting refined prompt options to the user based on their initial input. The prompt generation component serves itself by using the user's input as seed material to create multiple refined prompts, reducing the burden on the user to manually craft precise prompts while maintaining output quality
Data Source
AI summary
Systems, methods, and software are disclosed herein for guided prompt creation via foundation model integrations in application environments. In an implementation, a computing device displays a user interface of an application. Within the user interface, the computing device displays modifier key components for modifying a prompt to be submitted to a foundation model. The selection of any one of the modifier key components adds a corresponding modifier key to the prompt. The computing device obtains modifier values from the foundation model based on the prompt. The computing device also displays modifier value components in the user interface; the selection of a modifier value component adds a corresponding modifier value to the prompt. The computing device submits the prompt to the foundation model and displays a reply from the foundation model in the user interface.