Summary of Beyond-rag: Question Identification and Answer Generation in Real-time Conversations, by Garima Agrawal et al.
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations
by Garima Agrawal, Sashank Gummuluri, Cosimo Spera
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A decision support system is proposed to optimize customer contact center operations by leveraging retrieval augmented generation (RAG) systems and large language models (LLMs). The system identifies customer queries in real-time, retrieves answers directly from frequently asked questions (FAQs) databases if applicable, or generates responses via RAG. This approach reduces manual query reliance, providing agents with answers within 2 seconds. Deployed at Minerva CQ, the system improves efficiency, lowers average handling times, and operational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new tool is designed to help customer service agents work more efficiently. The system uses artificial intelligence to quickly find answers to common questions and provide them to agents in just two seconds. This helps reduce the time agents spend searching for information and makes it easier for them to give customers helpful responses. The tool also learns from past conversations to identify new frequently asked questions and provide answers. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
