Augmenting RAG Systems with Knowledge Graphs for Customer Service Q&A

In the world of customer service and technical support, providing rapid and accurate resolutions is paramount for ensuring customer satisfaction and loyalty. Traditional retrieval-augmented generation (RAG) systems have proven valuable for question-answering, but face limitations when dealing with complex, interlinked data sources like customer service ticketing systems.

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The Shortcomings of Basic RAG for Customer Service

While basic RAG can integrate external knowledge sources to ground language model responses, treating customer support tickets as disconnected text passages leads to key challenges:

1. Ignoring Metadata: Support tickets contain rich metadata like issue severity, context details, and links to related tickets that basic text embedding approaches fail to capture.
2. Missing Connections: Representing each ticket independently as vector embeddings loses the explicit relationships and structure within each ticket and across the ticket knowledge base.
3. Suboptimal Retrieval: Without encoding relationships, RAG struggles to accurately retrieve the most relevant evidence for answering questions that require reasoning over linked facts.