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

arXiv: https://arxiv.org/abs/2410.10136 · HTML: https://mlsanity.com/paper/2410.10136/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 4 of 4)

- [Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey](https://mlsanity.com/paper/2311.07914/) (2023)
- [Evaluating Retrieval Quality in Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.13781/) (2024)
- [RAG based Question-Answering for Contextual Response Prediction System](https://mlsanity.com/paper/2409.03708/) (2024)
- [Mindful-RAG: A Study of Points of Failure in Retrieval Augmented Generation](https://mlsanity.com/paper/2407.12216/) (2024)

## Built on by (most cited first, 9 of 9)

- [Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systems](https://mlsanity.com/paper/2601.07072/) (2026)
- [AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents](https://mlsanity.com/paper/2510.08149/) (2025)
- [ATACompressor: Adaptive Task-Aware Compression for Efficient Long-Context Processing in LLMs](https://mlsanity.com/paper/2602.03226/) (2026)
- [Reversing the Paradigm: Building AI-First Systems with Human Guidance](https://mlsanity.com/paper/2506.12245/) (2025)
- [CID-GraphRAG: Enhancing Multi-Turn Dialogue Systems through Dual-Pathway Retrieval of Conversation Flow and Context Semantics](https://mlsanity.com/paper/2506.19385/) (2025)
- [Redefining CX with Agentic AI: Minerva CQ Case Study](https://mlsanity.com/paper/2509.12589/) (2025)
- [HybridRAG: A Practical LLM-based ChatBot Framework based on Pre-Generated Q&A over Raw Unstructured Documents](https://mlsanity.com/paper/2602.11156/) (2025)
- [Safety as Computation: Certified Answer Reuse via Capability Closure in Task-Oriented Dialogue](https://mlsanity.com/paper/2603.21448/) (2026)
- [From Code to Requirements: Agentic Reverse Engineering of Business Rules at Enterprise Scale](https://mlsanity.com/paper/2609.22719/) (2026)

## Abstract (arXiv)

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. Our approach reduces reliance on manual queries, providing responses to agents within 2 seconds. Deployed in AI-powered human-agent assist solution at Minerva CQ, this system improves efficiency, reduces AHT, and lowers operational costs. We also introduce an automated LLM-agentic workflow to identify FAQs from historical transcripts when no predefined FAQs exist.