Implementation of Retrieval Augmented Generation (RAG) Model Using LLM: A RapidMiner-Based Approach

Generative AI technology, driven by Large Language Models (LLMs), is being increasingly utilized to overcome existing limitations. Retrieval-Augmented Generation (RAG) has emerged as an effective approach to reduce hallucination in LLMs by leveraging up-to-date and domain-specific knowledge beyond training data. However, most studies propose programming-based implementations. This research introduces a GUI-based RAG framework using RapidMiner, to construct RAG systems without programming proficiency. The methodology includes storing and retrieving embeddings with the Qdrant vector database and generating question-and-answer pairs via the OpenAI API. Practical demonstrations confirm the system’s effectiveness in real-world scenarios, offering a simpler and more efficient method for developing generative AI services with LLMs.