RAG Conversation Indexing for Faster Agent Response Guidance
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Solution Overview
Problem
Service agents struggle to determine relevant information from written guidelines during customer interactions, leading to inadequate responses and customer frustration.
Innovation Solution
Implementing a system that indexes past conversations and uses a large language model (LLM) to generate context-based responses and actions, including offline indexing with metadata and fuzzy similarity scoring to enhance relevance and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service agents read written guidelines during customer interactions, then they can provide information based on guidelines, but it is difficult for them to determine relevant portions quickly
Solution Approach 1:
The patent introduces an intermediary system consisting of an indexing service and a large language model that acts as a mediator between the service agent and the guidelines database. The indexing service pre-processes guidelines into structured data with metadata, and the LLM retrieves and synthesizes relevant portions based on conversation context, eliminating the need for agents to manually search through guidelines.
Solution Approach 2:
The indexing service performs preliminary action by pre-processing and indexing guidelines before they are needed during customer interactions. Guidelines are segmented, tagged with metadata, and organized in a structured format in advance, so that during live interactions, the system can quickly retrieve relevant portions without requiring time-consuming manual search.
2Loss of information
If service agents manually review written guidelines, then they can find relevant information, but customer satisfaction decreases due to delayed responses
Solution Approach 1:
The patent replaces the mechanical system of manual guideline review with an automated information retrieval system. The LLM automatically analyzes conversation context, queries the indexed guidelines database, and retrieves relevant information without requiring manual intervention from the service agent, thereby maintaining information completeness while dramatically improving response speed.
Solution Approach 2:
The system enables self-service by allowing the LLM to autonomously retrieve and synthesize relevant guideline information based on conversation context. The service agent simply needs to initiate the query, and the system independently handles the information retrieval, filtering, and presentation of relevant guidelines.
3Quantity of substance
If comprehensive guidelines are provided to service agents, then all necessary information is available, but agents cannot quickly identify relevant portions
Solution Approach 1:
The patent applies segmentation by dividing comprehensive guidelines into smaller, manageable segments with specific metadata tags. Each guideline is broken down into discrete units with associated metadata that describes its content, context, and applicability, making it easier for the system to retrieve and present only the relevant segments during customer interactions.
Solution Approach 2:
The patent changes the parameters of guideline storage by transforming unstructured text into structured data with multiple parameters including metadata tags, context indicators, and relevance weights. This parameterization allows the indexing service to efficiently query and retrieve guidelines based on multiple criteria simultaneously, greatly improving the ease of finding relevant information.
Data Source
AI summary
Systems and methods are provided for determining, at a server communicatively coupled to a database, whether any past conversation texts in the database include a message segment that used by one or more service agents in a conversation with a customer. The server samples m-furthest neighbors for at least one of the determined past conversation texts to determine usage data for the at least one of the determined past conversation texts. The samples are indexed for at least one of the determined past conversation texts for retrieval based on a context from the determined usage data to generate an index. A representation of a current conversation between the service agent and the customer is determined, and index is queried using the representation. A large language model (LLM) generates a response that is transmitted to the service agent to be used in the conversation with the customer.