Large Language Model Development and Implementation

Large Language Models are transforming how we interact with technology, moving beyond simple chatbots to complex reasoning engines. This course takes you from understanding the basic mechanics of text generation to building sophisticated, context-aware AI systems through written instruction and code-based examples. You will move from a conceptual understanding of how these models work to implementing them in real-world scenarios. By the end of this program, you will be able to design robust AI workflows that are accurate, efficient, and scalable. What you'll learn: - Understand the fundamental architecture of transformers and how models process and generate text. - Master advanced prompt engineering techniques including few-shot learning and chain-of-thought patterns. - Implement Retrieval-Augmented Generation (RAG) to connect models with your own external data sources. - Explore vector databases and semantic search for efficient information retrieval. - Apply evaluation metrics and testing patterns to ensure AI output reliability and safety. - Practice integrating models into software applications using modern API patterns and orchestration. The course begins with essential terminology and foundational concepts before progressing through the technical layers of model implementation and optimization. Each section focuses on clear, written explanations and practical code snippets to reinforce your learning. This course is designed for developers and tech-savvy beginners who want to move past basic prompting and start building with AI. No prior experience with machine learning is required. Begin your journey into the world of large language model engineering today.