AI Chatbot Prompt Templates for Predictable, Safe Responses
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Solution Overview
Problem
Large language models (LLMs) exhibit unpredictable behavior, including hallucinations and unsafe advice, making them unreliable for controlled interactions without proper constraint mechanisms.
Innovation Solution
A system using prompt templates selected, customized, and modified based on user data to control LLM interactions, with evaluation and adaptation of LLM output to ensure safe and targeted responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs are used to implement chatbots, then the chatbots appear more human-like and can perform natural language processing tasks, but the LLMs exhibit unpredictable behavior including hallucinations and unsafe advice
Solution Approach 1:
The patent introduces a prompt template as an intermediary between the user and the LLM. The prompt template includes structured sections (role definition, task description, constraints, output format) that mediate the interaction, guiding the LLM to produce more predictable and reliable outputs while maintaining natural language processing capabilities
Solution Approach 2:
The patent changes the parameters of LLM interaction by using temperature scaling (setting temperature to 0 for deterministic output) and structured prompt templates with defined sections. These parameter changes transform the LLM's behavior from unpredictable to controlled and reliable, while preserving its natural language processing strength
2Reliability
If prompt templates are selected, customized, and modified based on user data to control LLM interactions, then the interactions become more targeted and safe, but the system complexity increases
Solution Approach 1:
The patent segments the prompt template into distinct functional sections: role definition, task description, constraints, and output format. This segmentation makes the complex prompt management more manageable by breaking it into standardized components that can be independently configured and validated
Solution Approach 2:
The patent performs preliminary actions by pre-defining prompt templates with all necessary components (role, task, constraints, format) before actual LLM interaction. This preparation work is done in advance based on user data analysis, reducing the complexity during runtime interactions
3Reliability
If LLMs are controlled using prompt templates and evaluation mechanisms, then the output becomes more accurate and safe, but the processing time and resource consumption increase
Solution Approach 1:
The patent performs evaluation and prompt selection in advance based on user data, preparing the appropriate prompt template before the actual LLM interaction. This preliminary preparation reduces the time required during the interaction itself, as the system already has the optimized prompt ready
Solution Approach 2:
The patent changes the temperature parameter to 0, which makes the LLM output deterministic and faster to generate. This parameter change trades some creativity for speed and predictability, reducing processing time while maintaining accuracy through the structured prompt template
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.