Generative ML Prompt Refinement for 3D Design Context
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Solution Overview
Problem
Conventional generative machine learning models lack predictability and require iterative trial-and-error prompting, making it difficult for users to obtain accurate and detailed prompts for design modifications in computer-aided design applications.
Innovation Solution
A computer-implemented method that receives a prompt input indicating a design modification, determines a context input based on the 3D design attributes, and uses a generative ML model to generate a modified prompt input that describes the design modification with greater detail and accuracy, without the need for fine-tuning or additional training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional generative ML models are used for design modifications, then the models can perform operations, but the interactions require iterative trial-and-error prompting and are unpredictable
Solution Approach 1:
The system performs preliminary actions by automatically generating multiple candidate prompts and evaluating them before the user needs to interact. The engine pre-generates refined prompts based on the initial user input, so when the user views the suggestions, the work of iterative refinement has already been done in advance, eliminating the need for trial-and-error interactions.
Solution Approach 2:
The system implements feedback by evaluating generated prompts against the 3D design attributes and providing suggestions that incorporate information about what worked and what didn't in previous interactions. The engine uses feedback from the design context to improve subsequent prompt generations, making the system progressively more accurate and predictable.
2Productivity
If fine-tuning is used to improve model responses, then shorter and less detailed prompts can be obtained, but additional training data must be manually created
Solution Approach 1:
The system practices self-service by automatically generating refined prompts without requiring manual fine-tuning or training data creation. The prompt generation engine uses the 3D design attributes and interaction history to autonomously improve prompt quality, eliminating the need for users or developers to manually create training datasets while still achieving efficient interactions.
Solution Approach 2:
Instead of performing fine-tuning in advance (which requires manual training data creation), the system performs preliminary actions by pre-generating multiple prompt candidates and selecting the best ones based on automated evaluation against design attributes. This shifts the work from offline training to online generation, avoiding the complexity of manual data creation.
3Measurement precision
If iterative trial-and-error prompting is used, then users can eventually obtain satisfactory behavior, but a large amount of guesswork and time are required
Solution Approach 1:
The system eliminates time loss by performing the iterative refinement process in advance. The prompt generation engine pre-computes multiple refined prompt options based on the initial input and design context, so the user receives accurate prompts immediately without having to spend time on trial-and-error iterations.
Solution Approach 2:
The system uses feedback from design attributes and interaction patterns to directly generate accurate prompts without guesswork. The engine evaluates candidate prompts against the 3D design context and selects those most likely to produce satisfactory results, eliminating the need for users to engage in time-consuming trial-and-error guessing.
Data Source
AI summary
In various embodiments, a computer-implemented method for generating prompt inputs that describe design modifications includes receiving a first prompt input that indicates a first modification to a three-dimensional (3D) design, determining a first context input based on one or more attributes of the 3D design, and causing a first generative machine learning (ML) model to generate a second prompt input based on the first prompt input and the first context input, where the second prompt input indicates a second modification to the 3D design.