Customer Intent Classification with LLM-Trained Vector Embeddings
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Solution Overview
Problem
Existing customer service systems, particularly those using Large Language Models (LLMs), struggle with real-time processing and efficiency in classifying customer intent during interactions, leading to suboptimal assistance and increased session times.
Innovation Solution
Implementing a vector model-based system that creates embedding vectors from natural language inputs, allowing real-time classification of customer intent using a combination of AI models, including LLMs, to identify and redirect communications to appropriate departments or generate responses efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Large Language Models (LLMs) are used to classify customer intent, then the accuracy of intent classification is improved, but the processing time increases and real-time performance deteriorates
Solution Approach 1:
The system segments the intent classification task into two parts: using LLMs for accurate intent identification during training, and using a separate vector embedding model for real-time classification during operation. This segmentation allows each component to optimize for its specific function without compromising the other.
Solution Approach 2:
The system creates a copy of the LLM's intent classification capabilities in the form of a vector embedding model. This copy captures the essential classification functionality without requiring the computationally intensive LLM processing during real-time operations, thus maintaining speed while preserving accuracy.
2Measurement precision
If LLMs are used for real-time customer intent classification, then classification accuracy is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The system extracts the core classification capability from the complex LLM and separates it into a dedicated vector embedding model. This extraction removes the computational burden from real-time operations while preserving the essential intent classification function, thereby reducing system complexity during operation.
Solution Approach 2:
The vector embedding model acts as an intermediary between the customer communication input and the LLM-based intent classification. This intermediary layer translates communications into vectors that can be efficiently compared against stored intent representations, eliminating the need for direct LLM processing in real-time.
3Measurement precision
If LLMs process customer communications in real-time, then intent identification accuracy is improved, but the session time and response time increase
Solution Approach 1:
The system performs preliminary action by pre-computing and storing vector representations of various customer intents during training time. During actual customer interactions, the system simply compares incoming communications against these pre-computed vectors using cosine similarity, eliminating the need for time-consuming LLM processing during the actual service session.
Solution Approach 2:
The system creates a copy of the LLM's intent representations as static vector embeddings that can be rapidly queried during real-time operations. This copy allows the system to maintain high intent identification accuracy while responding much faster than direct LLM processing would permit.
Data Source
AI summary
A system for classifying customer communications based on customer intent receives a communication from a customer during a communication session. The communication can include natural language. The system can create an embedding vector based on the natural language of the communication. The first embedding vector can include a numerical representation of natural language extracted from the communication. The system can compare the embedding vector to multiple embedding vectors associated with intent classifications corresponding to a prediction of a type of assistance available for customers. The multiple embedding vectors can be created based on a model that is configured to identify customer intent. The system can identify which intent classification of the multiple intent classifications is associated with the embedding vector based on the comparison. The system can redirect the communication session to a sub-unit of the telecommunications network service provider based on the identified intent classification.