<![CDATA[Abstract The advancement of Artificial Intelligence (AI), particularly Large Language Models (LLMs), has enabled the development of more intelligent customer support chatbots. However, conventional LLM-based systems often suffer from issues such as hallucinated responses and limited context-awareness. This study aims to implement the Retrieval-Augmented Generation (RAG) model in a customer support chatbot to address the limitations of the existing customer service system at PT Gaws Inti Solusi. The system was developed using an Agile methodology through four iterative sprints, covering the setup of the development environment, RAG pipeline integration, UI/UX improvements, and system optimization. The proposed solution integrates LangChain, ChromaDB, and a locally deployed instruction-tuned LLM (mistral:instruct), along with a user-friendly chat widget and administrative dashboard. Evaluation results demonstrate that the RAG-based chatbot significantly improves response accuracy, reduces reliance on company leadership, and accelerates response times. The system effectively enhances the automation and efficiency of customer service processes, offering practical value for enterprise-level deployment Keywords: Chatbot, Retrieval-Augmented Generation, RAG, LLM, Customer Support, LangChain, ChromaDB, PT Gaws Inti Solusi Abstrak Perkembangan teknologi Artificial Intelligence (AI), khususnya Large Language Models (LLM), membuka peluang baru dalam pengembangan chatbot untuk layanan pelanggan. Namun, LLM konvensional masih menghadapi permasalahan seperti halusinasi informasi dan keterbatasan konteks. Penelitian ini bertujuan untuk mengimplementasikan model Retrieval-Augmented Generation (RAG) dalam sistem chatbot guna mengatasi keterbatasan sistem layanan pelanggan di PT Gaws Inti Solusi. Metode pengembangan menggunakan pendekatan Agile dengan empat sprint iteratif yang meliputi setup lingkungan pengembangan, integrasi pipeline RAG, peningkatan UI/UX, serta optimasi sistem. Sistem yang dibangun memanfaatkan LangChain, ChromaDB, dan model LLM lokal (mistral:instruct) yang diintegrasikan dengan dashboard admin dan widget chatbot interaktif. Evaluasi menunjukkan bahwa pendekatan RAG mampu meningkatkan akurasi respons, mengurangi ketergantungan pada pimpinan perusahaan, serta mempercepat waktu tanggap layanan pelanggan. Dengan demikian, sistem ini memberikan kontribusi nyata dalam otomatisasi dan efisiensi layanan informasi di lingkungan perusahaan Kata kunci: Chatbot, Retrieval-Augmented Generation, RAG, LLM, Layanan Pelanggan ,LangChain, ChromaDB, PT Gaws Inti Solusi]]>
