AI Prompt and Output Sanitization for Reliable LLM Inferencing
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Solution Overview
Problem
Existing AI models, particularly large language models (LLMs), pose risks due to unreliability, user harm, liability, and breaches of privacy and compliance, necessitating improved platforms for controlled and validated interactions.
Innovation Solution
Implementing a system with modules like model manager, consistency sampler, prompt manager, and safety filter to process and validate user inputs and outputs, ensuring sanitized interactions and adherence to user-defined constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used to process user inputs, then productivity and automation are improved, but reliability and safety deteriorate due to hallucinations and harmful outputs
Solution Approach 1:
The patent introduces intermediary components including a prompt manager that sanitizes user inputs before processing, and an output manager that validates and sanitizes model outputs. These intermediaries act as mediators between the user and the AI model, filtering out harmful content and ensuring compliance with safety standards while maintaining automated processing capabilities.
Solution Approach 2:
The system implements feedback mechanisms where the output of one AI model is fed into another model for validation and verification. This multi-stage feedback process allows the system to detect and correct hallucinations or harmful outputs by comparing results across multiple models and applying consistency checks before final output delivery.
2Adaptability or versatility
If AI models process user data, then service capability is improved, but privacy and security worsen due to data leakage risks
Solution Approach 1:
The patent applies preliminary action by sanitizing user inputs before they reach the AI model through a dedicated prompt manager. This pre-processing step removes or masks sensitive information, personally identifiable information, and potentially harmful content before the model processes the data, preventing privacy breaches at the source rather than attempting to protect data after processing.
3Reliability
If multiple validation and sanitization modules are added, then reliability and safety are improved, but device complexity increases
Solution Approach 1:
The patent implements universality by designing multi-functional modules that perform multiple tasks. For example, the prompt manager not only sanitizes inputs but also manages prompt construction and optimization. The output manager simultaneously validates outputs, sanitizes sensitive information, and formats responses. This multi-functionality reduces the number of separate components needed while maintaining comprehensive safety and validation capabilities.
Data Source
AI summary
A device may receive, via one or more processors, user input including a text prompt string. A device may process, via one or more processors, the text prompt string to generate a sanitized text prompt string. A device may receive a text output string corresponding to processing of the sanitized text prompt string using the one or more language models. A device may process, via one or more processors, the text output string to generate a sanitized text output string. A device may cause, via the one or more processors, the sanitized text output string to be transmitted via an electronic network.