Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations

Understand

In customer contact centers, human agents often struggle with long average handling times (AHT) due to the need to manually interpret queries and retrieve relevant knowledge base (KB) articles.

• While retrieval augmented generation (RAG) systems using large language models (LLMs) have been widely adopted in industry to assist with such tasks, RAG faces challenges in real-time conversations, such as inaccurate query formulation and redundant retrieval of frequently asked questions (FAQs).
• To address these limitations, we propose a decision support system that can look beyond RAG by first identifying customer questions in real time.
• If the query matches an FAQ, the system retrieves the answer directly from the FAQ database; otherwise, it generates answers via RAG.