Build Local LLM Q&A Systems with RAG and Docker

Unlock the potential of large language models for your specific data, maintaining full control and privacy without relying on external cloud services. This course empowers you to design, build, and deploy your own secure, local question-answering system using Retrieval Augmented Generation (RAG) principles and modern tools. What you'll learn: * Understand the foundational concepts of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). * Configure and manage Docker containers for local development and deployment of AI components. * Deploy and interact with local LLMs using Ollama for private, on-device inference. * Integrate the R2R framework to construct robust RAG-based Q&A applications. * Apply basic prompt engineering techniques to optimize RAG system responses. * Evaluate the effectiveness of a RAG system using simple metrics. The course starts with the essentials of LLMs and RAG, then progresses through hands-on setup of your local environment, model deployment, and component integration to build a complete application. This course is ideal for beginners eager to learn about AI, LLMs, and Docker, with no prior experience required in these areas. Begin your journey to building intelligent, private Q&A systems.