Fine-Tuned LLM Virtual Assistant for Enterprise Business Rules
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Solution Overview
Problem
Existing custom virtual assistants are not adept at handling enterprise-specific business rules, leading to a less desirable conversational experience for both enterprises and customers, as they cannot understand unique business configurations like pizza chains' offerings and rules.
Innovation Solution
A method and system using a fine-tuned large language model that determines use cases and executes dialog flows based on use case context, customer context, conversation context, business rules, and exit conditions to provide responses to customer utterances, enabling enterprise-specific virtual assistants to engage in human-like conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a general virtual assistant using a large language model is used, then natural and fluid conversations are achieved, but enterprise-specific business rules cannot be understood
Solution Approach 1:
The system applies local quality by integrating enterprise-specific business rules and configurations into the general LLM framework. The virtual assistant maintains the natural conversational abilities of the general LLM while adding localized enterprise knowledge through prompt engineering, allowing it to understand and apply specific business rules like pizza chain offerings and regulations without losing its conversational fluency.
2Adaptability or versatility
If a custom virtual assistant is developed with heavy development costs and lengthy timelines, then enterprise-specific use cases are handled, but development complexity and time increase
Solution Approach 1:
The system applies universality by creating a multi-functional virtual assistant that combines the capabilities of a general LLM with enterprise-specific functionality. Instead of building separate custom assistants for each enterprise, the system uses a single general LLM framework that can be adapted to handle multiple enterprise-specific use cases through prompt engineering and configuration, reducing development complexity while maintaining versatility.
Solution Approach 2:
The system applies preliminary action by pre-configuring the virtual assistant with enterprise-specific business rules, configurations, and knowledge bases before deployment. This allows the assistant to be ready to handle enterprise-specific use cases from the start without requiring extensive custom development, thereby reducing both development time and complexity while maintaining adaptability.
3Reliability
If rigorous development and testing are performed, then satisfactory conversational performance is achieved, but development time extends to months
Solution Approach 1:
The system applies copying by using prompt templates and pre-configured business rule structures that can be replicated across different enterprises. Instead of developing and testing each virtual assistant from scratch, the system uses standardized prompt patterns and configuration frameworks that can be copied and adapted, significantly reducing development time while maintaining reliable conversational performance through proven templates.
Data Source
AI summary
A virtual assistant server determines one or more use cases of one or more utterances received from one or more customer devices. The virtual assistant server executes one or more dialog flows corresponding to the one or more use cases of the one or more utterances, where the executing comprises providing one or more inputs to a fine-tuned large language model comprising: use case context, customer context, conversation context, business rules, and exit conditions. The virtual assistant server receives one or more outputs corresponding to the one or more inputs from the fine-tuned large language model, and determines one or more responses based on the one or more outputs. Subsequently, the virtual assistant server provides the one or more responses to the one or more customer devices.