Large Language Models have significantly improved natural language understanding and content generation. However, traditional LLMs operate using pre-trained knowledge and cannot reliably answer questions about proprietary organisational documents unless that information is incorporated into the model's context.
Retrieval-Augmented Generation (RAG) addresses this challenge by combining semantic document retrieval with LLM reasoning. Instead of asking an AI to "guess" an answer, a RAG system retrieves the most relevant document fragments and provides them as context for response generation.
This paper presents a comprehensive tutorial for deploying RAGFlow with Ollama on Ubuntu Linux. Topics include system architecture, deployment planning, model selection, security considerations, hardware sizing and practical business use cases. The tutorial also explores how SMEs and engineering organisations can use local AI infrastructure to improve productivity, knowledge management and decision support while maintaining control over sensitive information.