Retrieval-Augmented Generation (RAG) Chatbot for Handling Customer Complaints in the Energy Sector

Sari

Fast and accurate customer service is critical in the energy sector, especially for large-scale utilities like PLN. This study introduces a novel Retrieval-Augmented Generation (RAG)-based chatbot tailored for PLN’s internal operational context to automate customer complaint resolution in Bahasa Indonesia. In contrast to previous approaches that utilize only fine-tuned LLMs or retrieval-based question answering, our system uniquely integrates internal complaint records stored in internal database with a local Indonesian-optimized LLM through LangChain orchestration. The proposed architecture features temporal and linguistic preprocessing, vector embedding using FAISS, and a dynamic clarification-fallback mechanism, ensuring context-aware and grounded responses. This work contributes a scalable framework for deploying generative AI in high-stakes public utility settings, emphasizing data privacy, language fidelity, and real-time applicability. Evaluation results both simulated and human-reviewed demonstrate the chatbot’s effectiveness, achieving BLEU-4 of 46.5 and ROUGE-L of 0.63, with 92% of answers rated helpful. These findings underscore the model's potential to enhance customer experience and operational efficiency in Indonesia’s energy sector.