AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915542106898432 |
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| author | Laskar, Md Tahmid Rahman Tremblay, Julien Bouvier Fu, Xue-Yong Chen, Cheng TN, Shashi Bhushan |
| author_facet | Laskar, Md Tahmid Rahman Tremblay, Julien Bouvier Fu, Xue-Yong Chen, Cheng TN, Shashi Bhushan |
| contents | The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problems has been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact centers. To this end, we introduce AI Knowledge Assist, a system that extracts knowledge in the form of question-answer (QA) pairs from historical customer-agent conversations to automatically build a knowledge base. Fine-tuning a lightweight LLM on internal data demonstrates state-of-the-art performance, outperforming larger closed-source LLMs. More specifically, empirical evaluation on 20 companies demonstrates that the proposed AI Knowledge Assist system that leverages the LLaMA-3.1-8B model eliminates the cold-start gap in contact centers by achieving above 90% accuracy in answering information-seeking questions. This enables immediate deployment of RAG-powered chatbots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents Laskar, Md Tahmid Rahman Tremblay, Julien Bouvier Fu, Xue-Yong Chen, Cheng TN, Shashi Bhushan Computation and Language Artificial Intelligence Machine Learning The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problems has been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact centers. To this end, we introduce AI Knowledge Assist, a system that extracts knowledge in the form of question-answer (QA) pairs from historical customer-agent conversations to automatically build a knowledge base. Fine-tuning a lightweight LLM on internal data demonstrates state-of-the-art performance, outperforming larger closed-source LLMs. More specifically, empirical evaluation on 20 companies demonstrates that the proposed AI Knowledge Assist system that leverages the LLaMA-3.1-8B model eliminates the cold-start gap in contact centers by achieving above 90% accuracy in answering information-seeking questions. This enables immediate deployment of RAG-powered chatbots. |
| title | AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.08149 |