Local LLMs for Coding: Ollama, llama.cpp, and RAG

As artificial intelligence transforms software development, relying solely on cloud-based models can raise privacy concerns and incur high costs. This course teaches you how to bring the power of large language models directly to your own hardware. You will explore how to set up, configure, and utilize local LLMs to create secure, offline coding assistants and integrate them into your daily development workflows. What you will learn: Understand the core concepts behind large language models and local inference. Configure and run local models using Ollama and llama.cpp safely on your own machine. Build a foundational Retrieval-Augmented Generation (RAG) system using modern vector database concepts. Apply local LLMs to generate code, write tests, and automate routine development tasks. Practice prompt engineering techniques tailored for smaller, locally hosted models. Integrate offline AI autocomplete capabilities into your existing development environment. The course begins with essential AI terminology and foundational concepts before moving into practical setup instructions and written coding exercises. You will progress step-by-step from basic model execution to building a simple, secure local RAG pipeline. Designed for developers, backend engineers, and tech enthusiasts at a beginner level, this text-based course requires no prior machine learning experience. Start reading today to take control of your AI tools and build secure, local development assistants.