The manufacturing industry is undergoing a transformative change towards digitalization and advanced technologies. The management, organization and reuse of large, heterogenous amount of data generated during the whole manufacturing process remain key challenges. In parallel, human expertise is rarely converted into structured data that can be exploited for process improvement and daily troubleshooting. Although recent advances in Artificial Intelligence (AI) are promising, general-purpose language models exhibit well-known limitations when operating in regulated industrial domains. Responses may be hallucinated and not aligned with company procedures and standards, resulting in distorted information and struggling decisions-making. Retrieval-Augmented Generation (RAG) paradigm is based on a combination of information retrieval system and Large Language Models (LLMs) generation, coupling two fundamental approaches to ensure traceability into specific knowledge domain documentation.