This immersive four-week program equips developers and tech professionals with the practical skills to architect and deploy cutting-edge generative AI applications. Moving beyond theoretical concepts, participants will build functional AI systems spanning text generation, multimodal applications, intelligent knowledge retrieval, and multi-agent architectures. By the end of this hands-on course, you'll have developed your own AI application while mastering the latest frameworks, ethical considerations, and architectural patterns that define the emerging field of generative AI engineering.

The landscape of software development is undergoing a paradigmatic shift with the emergence of generative AI technologies. This course provides a structured pathway into this revolution, beginning with the fundamental mechanics of Large Language Models and progressing to sophisticated multi-agent systems. Throughout the program, participants will engage with both the theoretical underpinnings and practical implementations of generative AI, gaining hands-on experience with industry-standard tools and frameworks.

Week 1 establishes a foundation in text generation, exploring the mathematical principles behind vector embeddings, token prediction, and emergent behaviors in LLMs. Participants will implement their first AI applications using OpenAI's APIs, learning to craft effective system prompts and protect against prompt engineering attacks. Through practical exercises, students will understand how to structure AI conversations and implement function calling to expand AI capabilities beyond language generation.

Week 2 expands into multimodal AI applications, focusing on voice generation and real-time voice-to-voice agents. The course examines technical implementations of text-to-voice technologies through platforms like 11Labs, along with ethical considerations surrounding voice cloning and authentication. Participants will explore real-world applications across accessibility, education, and content creation while building their own multimodal AI projects.

Week 3 introduces Retrieval Augmented Generation (RAG) and vector search technologies, addressing the limitations of traditional LLMs through external knowledge integration. The curriculum covers vector embeddings, similarity measures, and the nuanced challenges of document chunking. Students will learn advanced techniques like Generation Augmented RAG (GARAGE) for multi-dimensional categorization and searching, applying these concepts to practical challenges like resume parsing and knowledge base construction.

Week 4 culminates with multi-agent systems, exploring how networks of specialized AI agents can collaborate to solve complex problems. The course examines advanced reasoning techniques, including Chain of Thought, Tree of Thoughts, and Graph of Thoughts, alongside memory management strategies for agent persistence. Through demonstration of frameworks like Autogen, participants will understand the coordination models and communication patterns that enable truly autonomous systems, preparing them to design their own multi-agent architectures.