Prompt Refinement Service for Higher-Quality Generative Output
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Solution Overview
Problem
Existing computer models, such as large language and vision-language models, often fail to receive sufficient or appropriate inputs, leading to inefficient training processes that require excessive computing resources and ineffective output generation.
Innovation Solution
A prompt refinement service that utilizes a library of prompt refiners to enhance user inputs before they are fed into generative models, optimizing the models' performance by selecting and aggregating the most suitable refiners based on similarity scores and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If computer models are trained on each potential input from the entity, then the model output quality improves, but the computing resources required and training time increase excessively
Solution Approach 1:
The prompt refinement service performs preliminary refinement of entity inputs before they are processed by the generative model. By pre-processing inputs with appropriate refiners selected based on similarity scores, the system prepares optimized prompts in advance, reducing the need for extensive model training on every potential input while maintaining high output quality
Solution Approach 2:
The system changes the parameters of the input prompt by applying different refiners that modify various aspects of the prompt (such as adding context, changing format, or enhancing specificity). This transformation of input parameters allows the model to generate better outputs without requiring retraining on each possible input variation
2Manufacturing precision
If computer models are trained on each potential input from the entity, then the model output quality improves, but the training process becomes overly complex and resource-intensive
Solution Approach 1:
The prompt refinement service acts as an intermediary between the entity input and the generative model. Instead of training the model on every potential input, the refinement service processes inputs through selected refiners, simplifying the overall system architecture and reducing training complexity while maintaining output quality
Solution Approach 2:
The system segments the input processing into distinct components: entity input, prompt refinement service with multiple refiners, and the generative model. This segmentation allows each component to specialize in specific tasks, reducing the complexity that would arise from training a single model on all possible inputs
3Speed
If the generative model receives basic entity input without refinement, then the processing speed is faster, but the output relevance and accuracy decrease
Solution Approach 1:
The system applies partial refinement by selecting and applying only the most relevant refiners based on similarity scores, rather than applying all possible refinements. This partial action maintains processing speed while sufficiently enhancing output relevance and accuracy
Data Source
AI summary
A prompt refinement service can be used to enhance output from a generative model. Entity input that indicates a request for a response can be received. A set of prompt refiners can be received. A prompt refinement service can identify a particular prompt refiner among the set of prompt refiners having a highest similarity to the entity input compared with other prompt refiners. The prompt refinement service can aggregate the particular prompt refiner with the entity input to generate an aggregated input. The prompt refinement service can transmit the aggregated input to the generative model to cause the generative model to return an output in response to the entity input.