Conversational Agent Prompts With In-Prompt Hallucination Management
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Solution Overview
Problem
Generative language models (LLMs) often produce inaccurate or nonsensical information with high confidence, undermining trust and reliability, and conventional approaches for updating models based on user feedback suffer from latency and resource-intensive re-engineering challenges.
Innovation Solution
Implementing chain of thought reasoning on the output (COTRO) to refine model-generated output within the prompt structure, reducing the risk of hallucination without requiring human feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches for updating models based on user feedback are used, then model accuracy can be improved, but latency and resource-intensive re-engineering challenges increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring the generative model with chain of thought reasoning capabilities and hallucination management mechanisms before deployment. The model is trained in advance to perform self-correction and verification tasks, eliminating the need for real-time feedback loops and iterative re-engineering, thus reducing latency while maintaining accuracy improvement
2Measurement precision
If conventional approaches for updating models based on user feedback are used, then model accuracy can be improved, but resource-intensive re-engineering challenges increase
Solution Approach 1:
The patent implements self-service by enabling the generative model to autonomously manage its own hallucinations through integrated chain of thought reasoning. The model performs self-correction, self-verification, and self-monitoring without requiring external feedback loops or manual re-engineering resources, significantly reducing computational overhead while maintaining accuracy
3Reliability
If chain of thought reasoning on the output is implemented, then reliability of generative model output is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the chain of thought reasoning process into distinct modular components: preliminary output generation, hallucination identification, correction reasoning, and final output synthesis. Each component is handled through separate prompt instructions, making the complex reliability improvement process manageable and structured without overwhelming system complexity
4Measurement precision
If in-prompt hallucination management is implemented, then accuracy of conversational agent is improved, but prompt processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-structuring prompts with embedded hallucination management instructions before the generative model processes user input. The prompts are configured in advance to guide the model through verification and correction steps, reducing the computational complexity during actual processing while maintaining high conversational accuracy
Data Source
AI summary
An example may provide a prompt associated with a first state of a conversational system to a first machine learning model. The prompt may include at least one instruction to cause the first machine learning model to use at least the first state to generate at least one second state and reasoning, and use the reasoning to generate second output. The at least one second state may be generated by the first machine learning model using the first state. The reasoning may include an explanation of how the first machine learning model generated the at least one second state. The second output may be generated using the first machine learning model and the prompt. Options may be provided for presentation via the conversational system. The options may include digital content generated using the first machine learning model and the second output.