Layered LLM Prompting for Transparent and Adaptive AI Decisions
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Solution Overview
Problem
AI models operate as 'black boxes', lacking transparency and accountability, which hinders trust, regulatory compliance, and adaptability, and traditional prompt engineering methods fail to provide structured decision-making insights.
Innovation Solution
Constructing a layered AI model with context-specific layers and variables, enabling traceable and adaptable decision-making processes by breaking down the model into interpretable layers and dynamically generating prompts to align with evolving guidelines and standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rule-based methods and signature-based detection techniques are used, then transparency is improved, but adaptability deteriorates
Solution Approach 1:
The patent segments the AI model into multiple interpretable layers, each processing specific aspects of the input data. This segmentation allows the system to maintain the adaptability of AI models while providing transparency by breaking down the decision-making process into discrete, explainable components that can be individually analyzed and validated.
Solution Approach 2:
The patent introduces an intermediary explanation layer that mediates between the black-box AI model and the end user. This intermediary component translates complex AI decisions into human-understandable explanations, thereby maintaining both the adaptability of the AI model and the transparency required for user trust and regulatory compliance.
2Adaptability or versatility
If AI models operate as black boxes, then adaptability is improved, but transparency deteriorates
Solution Approach 1:
The patent segments the AI model into multiple interpretable layers, each processing specific aspects of the input data. This segmentation allows the system to maintain the adaptability of AI models while providing transparency by breaking down the decision-making process into discrete, explainable components that can be individually analyzed and validated.
3Device complexity
If single-prompt methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments the evaluation process into multiple targeted prompts, each focusing on specific aspects of the AI model's performance. This segmentation enables precise measurement of different dimensions (accuracy, fairness, robustness, etc.) without requiring a single complex evaluation system, thereby maintaining simplicity while improving measurement precision.
Solution Approach 2:
The patent transitions from single-dimension evaluation to multi-dimensional evaluation by introducing multiple prompts that assess different aspects of AI model performance. This dimensional expansion allows comprehensive precision measurement across multiple criteria while keeping each individual prompt relatively simple and manageable.
4Ease of operation
If static evaluation methods are used, then ease of operation is improved, but adaptability deteriorates
Solution Approach 1:
The patent implements dynamic prompt generation that automatically adapts to different AI models, domains, and evaluation contexts. The system dynamically selects and modifies prompts based on the specific evaluation needs, maintaining ease of operation through automation while achieving high adaptability to various scenarios and requirements.
Data Source
AI summary
Systems and methods for constructing layered prompts to operate as input into a pre-trained large language model (LLM). The method involves obtaining a set of application domains in which the LLM will be used. Using these application domains, a set of guidelines is determined, defining operation boundaries for the LLM. A set of layers is determined, each associated with the guidelines and including variables representing attributes identified within those guidelines. Using these layers, a first layered prompt is constructed to test the initial operation boundaries of the guidelines and is supplied to the LLM to generate a set of responses. Based on the responses, a second layered prompt is dynamically constructed to test additional operation boundaries, ensuring iterative refinement and contextual relevance.