Category-Specific LLM Workflows for Accurate Live Conversation Responses
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Solution Overview
Problem
Current large language models (LLMs) in customer service environments are inefficient and cumbersome, often requiring repetitive user queries due to their inability to accurately determine user intent and provide satisfactory responses, leading to increased computational resource usage and poor customer experience.
Innovation Solution
An LLM is trained using a plurality of completed projects, categorized and clustered by type, to generate optimized workflows by analyzing agent-user conversations, eliminating redundancies, and refining responses to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If LLM is used to respond to user queries in customer service environment, then automated response capability is improved, but accuracy in determining user intent deteriorates
Solution Approach 1:
The system segments the response generation process into multiple specialized models, each trained on specific categories of projects and workflows. This allows each model to specialize in particular domains, improving intent determination accuracy while maintaining automated response capability across different customer service scenarios.
Solution Approach 2:
The system performs preliminary actions by training multiple specialized LLM models on categorized historical projects before actual customer service interactions. These pre-trained models with optimized workflows are ready to handle specific query types, enabling more accurate intent determination from the outset rather than relying on a single general-purpose model.
2Stability of the object's composition
If LLM responses are not utilized to refine the models, then model training data remains static, but model improvement and adaptation capability deteriorates
Solution Approach 1:
The system implements feedback loops where LLM responses and customer service outcomes are captured and used to continuously refine and retrain the specialized models. This allows the models to adapt and improve over time while maintaining stable training data foundations through systematic categorization and clustering of new feedback data.
Solution Approach 2:
The system transitions from static model training data to a dynamic system where training data evolves continuously. New customer interactions, feedback, and outcomes are systematically incorporated into the training datasets of specialized models, enabling continuous adaptation while maintaining organizational knowledge stability through structured categorization.
3Stability of the object's composition
If standard responses or scripts are provided to users, then response consistency is improved, but customer service experience deteriorates
Solution Approach 1:
The system applies local quality by providing different levels of response personalization based on the specific query category and context. Rather than uniform standard responses, the system selects from multiple specialized models that can provide appropriately tailored responses for different customer service scenarios, improving experience while maintaining consistency within each category.
Solution Approach 2:
The system changes parameters by dynamically selecting which specialized model to use based on the categorized input query. This allows the response characteristics (tone, detail level, approach) to vary according to the specific customer service context while maintaining overall system consistency through structured model selection and categorized workflows.
4Ease of operation
If repetitive queries are required due to unsatisfactory responses, then user clarification opportunity is improved, but computational resource usage deteriorates
Solution Approach 1:
The system performs preliminary actions by using multiple pre-trained specialized models to provide accurate responses on first contact, reducing the need for repetitive user clarifications. The optimized workflows in each specialized model are designed to handle specific query types efficiently, minimizing computational resource usage while improving user experience.
Solution Approach 2:
The system replaces the mechanical repetition of general-purpose LLM queries with a more efficient system of specialized models. Each specialized model is optimized for specific query categories, substituting the need for repetitive interactions with a single general model with multiple targeted, pre-trained models that can accurately handle diverse customer service scenarios in one attempt.
Data Source
AI summary
Systems and methods for using large language models (LLMs) to analyze category specific workflows for generating an optimized workflow that can be used in live conversations to provide a response are described. The methods include categorizing a plurality of projects into specific categories. The actions performed by agents for the plurality of projects are translated into workflows. The workflows are analyzed based on optimization factors and clustering options, such as including redundancies in workflow steps, using alternative solutions to a workflow step, determining whether any escalation performed is justified and if so, adopting escalation related steps. The workflows are consolidated and optimized into an optimized workflow that is tested and verified, and then used in a live conversation.