Uncertainty-Aware Prompt Optimization for Generative AI
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Solution Overview
Problem
Existing generative AI models are susceptible to failure modes such as hallucinations and adversarial attacks, and lack robust methods for estimating risk and optimizing input prompts, relying on human-engineered constraints and heuristics that limit their applicability and generalization.
Innovation Solution
The technology modifies and optimizes input prompts using risk estimates to minimize uncertainty, transforming user models into risk-aware variants that iteratively refine prompts to improve output quality without additional training or models, identifying highly uncertain regions and computing gradients of risk to guide prompt modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing generative AI models are used without risk estimation, then the models can operate with simple input processing, but they are susceptible to failure modes such as hallucinations and adversarial attacks
Solution Approach 1:
The patent introduces an uncertainty estimator as an intermediary component that wraps around the existing generative model. This estimator provides risk assessments without modifying the core model architecture, enabling reliability improvement while maintaining relative simplicity of the base model.
Solution Approach 2:
The uncertainty estimator leverages the internal state and parameters of the existing generative model to produce risk estimates. By utilizing the model's own computations and representations, the system achieves reliable risk assessment without requiring separate training data or additional complex infrastructure.
2Measurement precision
If automated prompt optimization is implemented without risk estimates, then the optimization process can proceed quickly with simple criteria, but it cannot reliably identify failure modes or highly uncertain regions
Solution Approach 1:
The patent replaces manual or heuristic-based prompt optimization with an automated gradient-based optimization system. By computing gradients of uncertainty with respect to input prompts and applying gradient descent, the system achieves precise uncertainty minimization without relying on time-consuming manual engineering or trial-and-error approaches.
3Extent of automation
If risk-aware model transformation is applied, then the model can provide reliable risk estimates and identify uncertain regions, but it requires additional computational processing
Solution Approach 1:
The patent extracts the risk estimation functionality as a separate uncertainty estimator module that wraps around the existing generative model. This extraction allows the core model to remain unchanged and efficient, while the added component provides automated optimization capabilities through uncertainty guidance.
Data Source
AI summary
Methods of modifying an input, such as a prompt, for a machine learning model are disclosed. In at least some instances, the method includes transforming a received user model into a risk-aware model, applying that risk-aware model to a received input, and receiving, based on the application of the risk-aware model, output and corresponding risk values. In turn, the method includes determining whether the corresponding risk values, or an aggregate of such values, are less than a predetermined threshold level. If the values or aggregate are greater than the predetermined threshold level, then a process is performed to modify the input to minimize the corresponding risk values. The risk-aware model is iteratively applied to the modified input and the modification process continues to be performed until the corresponding risk values are less than the predetermined threshold level. Other methods, and systems for performing any methods disclosed, are also provided.