AI Chatbot Control Using Prompt Templates and Safety Routing
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Solution Overview
Problem
Large language models (LLMs) exhibit unpredictable behavior, including hallucinations and providing unsafe advice, making them unreliable for controlled interactions without proper constraint mechanisms.
Innovation Solution
Implementing a controlled artificial intelligence chat environment using prompt templates that are selected, customized, and modified based on user data, along with evaluating LLM output to adapt and fine-tune the model, ensuring constrained and safe interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used to implement chatbots with human-like interactions, then the chatbot appears more natural and versatile, but the system becomes unpredictable and may produce harmful outputs
Solution Approach 1:
The system segments the chatbot functionality into multiple specialized models: a primary LLM for generating human-like responses, a safety classifier for detecting harmful content, and a routing mechanism for directing queries to appropriate specialized models. This segmentation allows each component to focus on its specific function while collectively ensuring safe and reliable operation.
Solution Approach 2:
The patent introduces intermediary components between the LLM and user interactions, including a safety classifier that acts as a filter to block harmful outputs, and a routing mechanism that mediates query distribution to appropriate specialized models. These intermediaries maintain the versatility of LLM interactions while ensuring reliability through controlled output validation.
2Adaptability or versatility
If LLMs operate without control structures, then they can provide flexible and creative responses, but they may make up data and provide dangerous advice
Solution Approach 1:
The system implements feedback mechanisms where the safety classifier continuously monitors LLM outputs and provides corrective signals when harmful content is detected. The routing mechanism also provides feedback by directing queries to specialized models when appropriate, creating a closed-loop control system that maintains response flexibility while preventing harmful outputs through real-time validation and correction.
Solution Approach 2:
The patent applies preliminary anti-action by pre-configuring safety classifiers with knowledge of harmful patterns and pre-routing certain query types to specialized models before the LLM generates responses. This proactive approach prevents hallucinations and unsafe advice by establishing control structures in advance that constrain inappropriate behavior while preserving legitimate response flexibility.
3Measurement precision
If multiple specialized models are used for different query types, then accuracy for specific tasks improves, but system complexity increases
Solution Approach 1:
The routing mechanism serves multiple functions: it routes queries to appropriate specialized models, validates outputs for safety, and manages the coordination between different models. This multi-functional approach consolidates what could be separate complex components into a single unified system that handles task-specific accuracy while simplifying overall model management through centralized control.
Data Source
AI summary
A system uses a large language model (LLM) to implement a controlled artificial intelligence chat environment. The system may control interaction with the LLM using prompt templates that may be selected, customized, and/or modified based on information known about the user with whom the LLM will be interacting. Further, the system may evaluate output of the LLM to make changes to the LLM, the prompt templates, and so on. In some implementations, the system may use evaluation training data to adapt and fine-tune the LLM and/or another language model to evaluate output of the LLM in order to evaluate the efficacy of the chronic condition and/or disease management coaching path(s), and make improvements to the online or offline implementation of the language model in the future.