LLM Recommendation Grounding for Context-Aware Action Sequences
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Solution Overview
Problem
Users of complex systems face challenges in determining effective sequences of actions due to the need for extensive research, expertise, and trial-and-error, with existing AI-based solutions lacking industry-specific context and resulting in only marginally improved outcomes.
Innovation Solution
A model architecture that uses a large language model (LLM) to ingest historical system data, transforming it into labeled, natural language descriptions, and generating contextually enhanced recommendations for sequences of actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users rely on extensive research and manual analysis with statistical tools to determine effective sequences of actions, then the quality and expertise level of recommendations can be improved, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between raw system data and decision-makers. This intermediary automatically processes domain-specific data, performs analysis, and generates actionable recommendations, eliminating the need for users to manually conduct extensive research while maintaining high recommendation quality through advanced algorithms and machine learning models.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated AI-based systems. Instead of users manually analyzing data with statistical tools, the system uses machine learning models and natural language processing to automatically generate recommendations, significantly reducing time consumption while maintaining or improving recommendation quality.
2Loss of time
If existing AI-based solutions are used to generate recommendations, then time consumption is reduced, but the lack of industry-specific context results in only marginally improved outcomes
Solution Approach 1:
The patent applies local quality by customizing the AI system with domain-specific knowledge and industry-specific context. Instead of using a generic AI model, the system incorporates specialized data, terminology, and contextual understanding relevant to specific industries, thereby improving recommendation quality while maintaining the time efficiency benefits of automation.
Solution Approach 2:
The patent changes key parameters of the AI system by integrating domain-specific data and contextual information into the model. This involves adjusting the training data, feature sets, and contextual parameters to reflect industry-specific characteristics, thereby enhancing the reliability of recommendations while preserving the automated time-saving benefits.
3Reliability
If users manually analyze available data to craft effective strategies, then context-aware and accurate recommendations can be achieved, but the operational complexity and expertise requirements increase
Solution Approach 1:
The patent enables the system to serve itself by automatically performing data analysis, strategy formulation, and recommendation generation without requiring user intervention in complex analytical processes. The AI system autonomously processes domain-specific data, applies relevant contextual knowledge, and produces accurate recommendations, thereby maintaining high accuracy while significantly reducing operational complexity for users.
4Reliability
If extensive training and experience are developed through trial and error, then expertise and competence are improved, but the time investment and opportunity cost increase
Solution Approach 1:
The patent applies preliminary action by pre-training AI models with extensive domain-specific knowledge and contextual information before deployment. Instead of requiring users to invest time in trial-and-error learning, the system performs the learning and expertise development in advance during the model training phase, thereby providing expert-level recommendations immediately upon deployment without requiring user training time.
Data Source
AI summary
Certain aspects and features of the present disclosure relate to providing contextually grounded recommendations using a large language model. For example, a method involves receiving domain specific data for a simulation and transforming the domain specific data into a labeled, natural language description of the domain specific data. The method also involves providing the labeled, natural language description and a classification task prompt with interaction history to a large language model (LLM) to generate a contextually enhanced LLM configured to produce context-aware output. The method further involves outputting, using the contextually enhanced LLM, an interactive list of scored actions corresponding to the simulation. The interactive list can be used to produce a sequence of actions to direct a process or control a machine.