Shape the Future with Generative AI. Build. Innovate. Lead.
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.
What you will learn?
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.
Session 1: Introduction to Generative AI and Text Models
(Optional) Session 1: Introduction to generative AI and text models
Foundations of Generative AI & Language Models: Foundations of Generative AI & Language Models
Prompt Engineering & Optimizing Model Outputs: Introduction to prompt engineering: Crafting effective prompts
Advanced Techniques in Text Generation: Working with AI for Content Generation
Customizing Language Models for Specific Applications: Introduction to fine-tuning LLMs
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.
Session 2: Multimodal AI: Working with Audio and Image Generation
(Optional) Session 2: Multimodal AI: Working with Audio and Image Generation
Deep Dive into AI-Generated Audio: Introduction to speech-to-text + text-to-speech models
Multimodal AI: Working with Audio and Image Generation: Combining Image & Audio in Multimodal Applications
Building Interactive Multimodal Interfaces: Designing user experiences for multimodal AI applications
Customizing Multimodal Models: Fine-tuning and customizing AI models for specific needs
Deploying Multimodal AI Applications: Deployment strategies for multimodal AI applications
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.
Session 3: Retrieval Augmented Generation and its Applications
(Optional) Session 3: Retrieval Augmented Generation and its Applications
Introduction to Retrieval-Augmented Generation (RAG): What is RAG? Understanding the fusion of retrieval and generation
Understanding Vector Databases: Fundamentals of embeddings: How AI represents text as vectors
Building a RAG Pipeline from Scratch: Architecting a simple RAG system step by step
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.
Session 4: Multi-Agent Systems and Complex Reasoning
(Optional) Session 4: Multi-Agent Systems and Complex Reasoning
Introduction to Multi-Agent Systems (MAS): What are Multi-Agent Systems? Understanding agent-based AI
Multi-Agent Architectures and Design Patterns: Scaling Multi-Agent Systems for Production
Reasoning and Problem-Solving in MAS: Distributed decision-making in multi-agent environments
Cohort Demo Day: Showcasing Your Real-world Deployment Strategies and Best Practices
Who should take this course?
AI-Curious Devs and Engineers: Developers and engineers with experience using third-party REST APIs who want to build advanced AI-driven applications.
Ambitious Career-Changers: Individuals with basic coding knowledge and API experience who are eager to break into AI development.
CTOs, Team Leads, and Managers: Technical leaders who see AI as a strategic tool for their teams and want to learn how to build and deploy better AI-powered apps.
Product Managers or Designers: Professionals seeking technical fluency in AI to make generative AI a crucial part of their product vision.
