AI Response Filtering via Segmented Model Architecture
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Solution Overview
Problem
Current conversational AI systems face reliability and security issues due to vulnerabilities like prompt injection attacks, off-topic responses, and inability to fact-check, leading to inappropriate outputs and disclosure of sensitive information, with brute-force approaches like increasing model size introducing new problems such as reliability, bias, and computational resource inefficiencies.
Innovation Solution
A novel conversational AI system architecture that includes specialized filtering modules to assess threats, distinct conversational skill modules for unique abilities, and quality control filters to prevent inappropriate responses, using structured codes for communication and compartmentalizing skills to maintain conversation flow and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute-force approaches like increasing model size are used to improve AI reliability, then some weaknesses may be fixed in the short term, but new problems arise regarding reliability, bias, efficiency and adaptability
Solution Approach 1:
The patent segments the AI system into multiple independent modules: a base AI model, a filter AI model for security assessment, a quality control AI model for response validation, and a database for historical data. Each module performs a specific function, allowing the system to achieve high reliability without requiring a single overly complex model. The segmentation enables independent optimization of each component while maintaining overall system coherence.
Solution Approach 2:
The patent introduces intermediary models between the base AI model and the user interface. The filter AI model acts as an intermediary to assess security threats before processing, and the quality control AI model serves as an intermediary to validate responses before delivery. These intermediary layers prevent direct exposure of the base model to malicious inputs and ensure consistent, reliable outputs without increasing the base model's complexity.
2Reliability
If brute-force approaches like increasing model size are used to improve AI reliability, then some weaknesses may be fixed in the short term, but computational resources and energy consumption increase significantly
Solution Approach 1:
The patent divides computational tasks across multiple specialized models rather than relying on a single large model. The filter AI model handles security assessments with lower computational demand, the quality control AI model validates responses efficiently, and only the base AI model performs core generative tasks. This segmentation reduces overall energy consumption while maintaining high reliability through coordinated operation of specialized components.
Solution Approach 2:
The filter AI model performs preliminary security assessment of inputs before they reach the base AI model, preventing malicious or inappropriate inputs from consuming computational resources. The quality control AI model conducts preliminary validation of responses before delivery, ensuring reliability without requiring the base model to reprocess all inputs. These preliminary actions filter out problematic cases early, reducing overall computational burden.
3Adaptability or versatility
If increasing model size is used to improve AI performance, then model capabilities increase, but the model becomes more susceptible to bias and overfitting to niches
Solution Approach 1:
The patent segments functional capabilities across different models: the base AI model provides general conversational capabilities, the filter AI model handles security and appropriateness assessment, and the quality control AI model ensures response reliability. This segmentation allows each model to be trained for its specific function with appropriate data, reducing bias and overfitting compared to a single large model that must handle all scenarios. The database of historical data further supports this by providing targeted training material for each specialized model.
Solution Approach 2:
Each AI model in the system is trained with specialized quality and purpose: the base model focuses on conversational fluency, the filter model on security and policy compliance, and the quality control model on response accuracy and appropriateness. This local quality approach ensures that each model is optimized for its specific function rather than attempting to be universally capable, thereby reducing bias and improving reliability for each particular task.
4Adaptability or versatility
If increasing model size is used to improve AI performance, then model capabilities increase, but complexity increases which obscures explanations and debugging efforts
Solution Approach 1:
The patent segments the AI system into distinct, independently testable modules with clear input-output interfaces. Each module can be debugged and explained separately: the base model for generation, the filter model for security assessment, and the quality control model for validation. This segmentation makes debugging much easier compared to a single large model, as issues can be isolated to specific modules and addressed independently. The database of historical data provides reference material for debugging and explaining model behavior.
Data Source
AI summary
An example system may comprise a control module associated with a computing device. After receiving input from a user device the control module may provide the input to a filter AI model. When the filter AI model returns an indication that the input is legitimate, the control module may create a prompt and provide it to an AI model, which determines at least one configuration a response to the prompt should be configured and return at least one short code associated with the at least one determined configuration. The control module may provide the prompt to at least one tuned AI model associated with the associated configuration(s). The control module may receive a response from the AI model(s). Before the control module may transmit the response to the user device, a second filter AI model may return an indication that the response does not violate established standards of quality.