Agricultural Drone Technical Consultation Chatbot Based on Generative AI with RAG Approach
DOI:
https://doi.org/10.35145/dkv8x122Keywords:
Chatbot, Agricultural Drone, Generative AI, Retrieval-Augmented Generation, RAGAbstract
This research aims to develop an agricultural drone technical consultation chatbot system capable of providing accurate and responsive solutions to address farmer operational downtime. The research was conducted at PT. Smart Tech Solution International during the period of August to November 2025. The research method applied was Rapid Application Development (RAD), comprising the requirement planning, user design, construction, and cutover phases. The system was built using GPT-3.5 Turbo Large Language Model technology and the LangChain framework with ChromaDB vector database integration. Research results through comparative testing (A/B Testing) show that the RAG architecture significantly improved answer quality compared to the baseline model. Semantic Similarity increased by 59%, from 51% to 81%. The system recorded a Recall@5 value of 96% with an average response time of 2.41 seconds. Evaluation through User Acceptance Test (UAT) produced an average satisfaction score of 4.05 on a scale of 5.0. The conclusion of this research is that the implementation of the RAG method is effective in minimizing AI hallucinations and providing a reliable technical assistant to support after-sales service efficiency in the precision agriculture sector.
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