LLM Workflow Tuning for Faster Logistics Customer Responses
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Solution Overview
Problem
Customer service responses in logistics platforms often require extended response times and manpower due to human interaction, leading to reduced productivity and customer satisfaction.
Innovation Solution
Implementing a Large Language Model (LLM) and interpreter module to convert natural language inquiries into structured language, using a knowledge base and application programming interface (API) data to generate responses, and integrating a Chain of Thought (CoT) reasoning model to enhance logic reasoning and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human customer service representatives are used to handle inquiries, then judgment and policy-based solutions can be provided, but response time increases and productivity decreases
Solution Approach 1:
The patent introduces an LLM-based intermediary system that acts as a mediator between customer inquiries and human representatives. The LLM processes natural language queries, extracts intent and context, and generates structured responses or action items, reducing the need for direct human intervention while maintaining solution quality.
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated LLM system. The LLM uses natural language processing and reasoning capabilities to perform tasks previously requiring human judgment, such as understanding customer intent, retrieving relevant information, and generating appropriate responses.
2Reliability
If human customer service representatives are deployed, then complex inquiries can be resolved using judgement, but manpower requirements increase and productivity decreases
Solution Approach 1:
The patent enables self-service through the LLM system, which autonomously processes customer inquiries without requiring human representatives for routine tasks. The LLM independently performs intent recognition, context analysis, information retrieval, and response generation, allowing the system to serve itself for common customer service operations.
Solution Approach 2:
The patent changes the operational parameters of the customer service system by transitioning from human-based processing to LLM-based processing. This parameter change enables parallel processing of multiple inquiries simultaneously, increasing service throughput while maintaining resolution quality through the LLM's advanced natural language understanding capabilities.
3Loss of time
If LLM is used to automate response generation, then response time decreases and productivity increases, but training data requirements and system complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-training the LLM with synthetic training data that captures various customer service scenarios, intents, and contexts. This pre-training prepares the LLM to handle diverse inquiries efficiently, reducing response latency during actual operations while managing system complexity through advance preparation.
Solution Approach 2:
The patent segments the customer service system into distinct functional modules handled by the LLM: intent recognition, context analysis, information retrieval, and response generation. This segmentation allows each component to be optimized independently, managing overall system complexity while achieving fast response times through specialized processing.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for tuning a Large Language Model. Various aspects may include receiving a query from a user; in response to the query, identifying a workflow of a plurality of workflows. Aspects may also include extracting from the workflow: at least one intent parameter, at least one context parameter, and at least one action parameter. Aspects may also include providing the at least one intent parameter, the at least one context parameter, and the at least one action parameter to the LLM structured in the coding syntax for use by the LLM. Aspects may also include retrieving contextual data from an application programming interface (API) associated with the query. Aspects may also include tuning the LLM, wherein tuning the LLM comprises training the LLM with synthetic training data; and providing an action item to a user via the LLM.