Generative AI and LLM Fundamentals for Developers

Generative artificial intelligence is reshaping how we build software, analyze data, and solve complex problems. To stay competitive, technical professionals must understand the mechanics behind these systems rather than just using them as black boxes. This course provides a clear, step-by-step pathway to mastering the core concepts of large language models and generative workflows. You will transition from a curious developer to a practitioner capable of designing and implementing modern AI-driven solutions. By focusing on the architectural principles and practical integration patterns, you will gain the confidence to select, prompt, and orchestrate models for real-world applications. What you'll learn: - Understand the foundational architecture of transformers, tokens, and neural networks. - Master advanced prompt engineering techniques to guide model behavior reliably. - Implement Retrieval-Augmented Generation (RAG) patterns using vector databases. - Evaluate and mitigate common LLM limitations like hallucinations and bias. - Configure API integrations to connect language models with external data sources. - Apply modern orchestration frameworks to build autonomous agentic workflows. The course begins with essential terminology and foundational AI concepts before moving into structured text-based exercises on prompt design, vector search, and application integration. You will study complete code implementations and architectural blueprints that show you exactly how to build secure, scalable AI pipelines. This course is designed for software developers, data professionals, and technology enthusiasts who want a structured, beginner-friendly introduction to generative AI without needing prior machine learning experience. Start reading today to build your first intelligent, model-driven applications.