Iterative Prompt Loop Using Evaluation Feedback Across AI Models
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Solution Overview
Problem
Conventional prompt engineering techniques are time-consuming and lack generalizability across different machine learning models, often requiring trial and error to achieve desired outputs, and fail to consistently elicit the intended behavior from machine learning models.
Innovation Solution
A computing system that iteratively generates a prompt through a loop process, using evaluation criteria to score and replace prompts, allowing for the automatic refinement of prompt generation, incorporating stochastic search algorithms and test inputs to ensure consistency and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional prompt engineering techniques are used, then manual prompt construction is possible, but the process is time-consuming and lacks generalizability
Solution Approach 1:
The system enables automatic prompt generation and optimization through iterative loops where the machine learning model generates candidate prompts, evaluates them against criteria, and refines them automatically. This self-service mechanism eliminates the need for manual trial-and-error prompt construction, significantly improving productivity while reducing manual intervention.
Solution Approach 2:
The patent implements a feedback loop where evaluation criteria are used to score candidate prompts, and the results feed back into generating improved prompts in subsequent iterations. This continuous feedback mechanism allows the system to automatically refine prompts until they meet the desired performance thresholds, resolving the contradiction between automated generation and quality assurance.
2Reliability
If trial and error methods are used, then prompt optimization is possible, but consistency across different machine learning models is poor
Solution Approach 1:
The system employs universal evaluation criteria that can assess prompt performance across different machine learning models. By using a standardized evaluation framework that works with multiple model types, the system achieves both reliability in prompt optimization and adaptability across different models, eliminating the need for model-specific prompt tuning.
Solution Approach 2:
The patent performs preliminary prompt generation and evaluation before final selection, using iterative loops to pre-optimize prompts based on evaluation criteria. This preliminary action ensures that prompts are refined in advance to meet consistency requirements, so when deployed across different models, they maintain reliable performance without requiring additional adaptation.
3Manufacturing precision
If iterative prompt generation with evaluation is implemented, then prompt quality improves, but computational resources and time increase
Solution Approach 1:
The system implements partial iteration by stopping the prompt generation loop when prompts meet predefined performance thresholds or after a maximum number of iterations. This partial action approach ensures sufficient prompt quality improvement while preventing excessive computation time, resolving the contradiction between precision and time loss.
Solution Approach 2:
The patent dynamically adjusts evaluation criteria and iteration parameters based on prompt performance and computational resources. By changing parameters such as evaluation strictness, number of iterations, and candidate prompt quantities, the system optimizes the balance between prompt accuracy and generation time, achieving high precision without excessive time loss.
Data Source
AI summary
A computing system including one or more processing devices configured to receive prompt generation instructions that specify an initial prompt and a prompt evaluation criterion. In each of a plurality of iterations of a prompt generation loop, the one or more processing devices are further configured to generate candidate prompts at least in part at a machine learning model. The candidate prompts are generated based on a current-iteration prompt that is initialized as the initial prompt in a first iteration. As specified by the prompt evaluation criterion, the one or more processing devices are further configured to compute respective evaluation scores associated with the candidate prompts. Based on the evaluation scores, the one or more processing devices are further configured to replace the current-iteration prompt. The one or more processing devices are further configured to output a final prompt generated in a final iteration.