Prompt Refinement Model for Efficient Generative AI Collaboration

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Solution Overview

Problem

Existing interactions with machine-learned generative models are inefficient due to the one-shot nature of user queries, leading to high computational resource consumption and time spent refining prompts for desired outputs, as small variations in inputs result in significant differences in model outputs.

Innovation Solution

A machine-learning collaboration system that includes a prompt refinement model to analyze and refine user prompts using a datastore of prompt analysis data, enabling efficient generation of improved prompts and reducing repetitive queries by leveraging prompt history and performance data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If users manually refine prompts for machine-learned generative models, then output quality improves, but time consumption increases

Engineering Contradiction:

Improveoutput qualityVSAvoidtime consumption

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of prompt effectiveness before final generation, using the prompt refinement model to evaluate and optimize prompts in advance. This prevents time-wasting iterative manual refinements by preparing optimized prompts beforehand based on historical performance data.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback loops where prompt refinement data and performance metrics are continuously collected, stored in the datastore, and used to train and improve the prompt refinement model. This feedback mechanism enables the system to learn from past interactions and automatically improve prompt quality over time without manual intervention.

Inventive Principle:

Principle #23Feedback

2Manufacturing precision

If users perform multiple iterative queries to refine prompts, then desired output is achieved, but computational resource consumption increases

Engineering Contradiction:

Improveoutput qualityVSAvoidcomputational resource consumption

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The prompt refinement model performs preliminary optimization of prompts before they are submitted to the generative model. By pre-refining prompts using the trained model and historical data, the system reduces the need for multiple iterative queries to the computationally expensive generative model, thereby lowering overall resource consumption.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The prompt refinement model acts as an intermediary between the user and the generative model. It processes and optimizes prompts before they reach the generative model, filtering out ineffective queries and reducing the total number of interactions needed with the resource-intensive generative model.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If the system stores and processes prompt history data, then prompt refinement accuracy improves, but system complexity increases

Engineering Contradiction:

Improveprompt refinement accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system stores copies of prompt-refinement pairs and performance data in a datastore for future reference. By maintaining historical records of successful prompt refinements and their outcomes, the system can retrieve and apply proven refinement strategies without recalculating from scratch, improving accuracy while managing complexity through organized data storage.

Inventive Principle:

Principle #26Copying

4Productivity

If the system uses a machine-learned prompt refinement model, then prompt generation speed improves, but initial setup and training time increases

Engineering Contradiction:

Improveprompt generation speedVSAvoidtraining time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary training of the prompt refinement model using historical prompt data before deployment. This upfront training investment enables the model to quickly generate refined prompts during operation, achieving high generation speed that compensates for the initial training time expenditure.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250315428A1Machine-Learning Collaboration System

Publication Date: 2025.10.09 GOOGLE LLC

AI summary

Aspects of the disclosed technology include computer-implemented systems and methods for machine-learned collaboration for prompt editing. A machine-learned system includes one or more machine-learned generative models configured to generate one or more outputs in response to an input prompt, a prompt refinement datastore configured to store prompt analysis data and prompt refinement data for a plurality of prompts provided to the one or more machine-learned generative models, and a machine-learned prompt refinement model. The machine-learned prompt refinement model is configured to receive an input including data indicative of a particular prompt issued to the machine-learned generative model and generate one or more outputs including prompt refinement data for the particular prompt based at least in part on the prompt analysis data and prompt refinement data in the prompt refinement datastore.