Vol. 7 No. 1 (2025), Articles
Vol. 7 No. 1 (2025)

Intelligent FAQ Chatbot: A User-Centric Approach using Large Language Models

Articles
Published 30-04-2025
Sai Jyothi B.
Information Technology, Vasireddy Venkatadri Institute of Technology (VVIT), Jawaharlal Nehru Technological University (JNTUK), Guntur, India
Vyshnavi U.
Information Technology, Vasireddy Venkatadri Institute of Technology (VVIT), Jawaharlal Nehru Technological University (JNTUK), Guntur, India
Kavya Vasanthi Y.
Information Technology, Vasireddy Venkatadri Institute of Technology (VVIT), Jawaharlal Nehru Technological University (JNTUK), Guntur, India
Sai Deepika R.
Information Technology, Vasireddy Venkatadri Institute of Technology (VVIT), Jawaharlal Nehru Technological University (JNTUK), Guntur, India
Ramya Sindhu G.
Information Technology, Vasireddy Venkatadri Institute of Technology (VVIT), Jawaharlal Nehru Technological University (JNTUK), Guntur, India
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Keywords

FAQ Chatbot
Large Language Models (LLMs)
Contextual Understanding
Personalization
Information Retrieval
User Interaction History
Automated Systems
Adaptive Learning

How to Cite

B., Sai Jyothi, Vyshnavi U., Kavya Vasanthi Y., Sai Deepika R., and Ramya Sindhu G. 2025. “Intelligent FAQ Chatbot: A User-Centric Approach Using Large Language Models”. Journal of Artificial Intelligence and Capsule Networks 7 (1): 78-93. https://doi.org/10.36548/jaicn.2025.1.006.
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Abstract

The use of increasingly automated systems for tasks like customer support and information retrieval is considerably changing industries such as education, healthcare, and e-commerce. Typical FAQ chatbots often rely on rule-based or keyword-matching algorithms, which limit their ability to address complex questions, adapt to personalized contexts, and learn from interactions. This undertaking introduces a novel chatbot for frequently asked questions, which uses advanced LLMs to tackle these issues. Gemini is an advanced LLM, and the LangChain allows for advanced context management. This system improves comprehension of user intent and also generates natural, contextually relevant responses, progressing beyond static, database-dependent answers. It uses user-specific data, such as interaction history and preferences, to customize different interactions and generally increase user satisfaction. This framework directly addresses scalability challenges as well as delivers remarkably smart, user-centric automation. It does so for contemporary customer support along with information retrieval applications through thoughtfully combining contextual understanding, in addition to personalization. This approach illustrates the transition from prescriptive, rule-driven systems to more comprehensive, learning-oriented driving systems. It enhances the system’s effectiveness and user experience while encouraging the growth of advanced, user-friendly automation help systems.

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