In this video, I lay out my GenAI Roadmap for 2025—the exact learning path I’d follow if I had to go from fundamentals to building production-grade GenAI systems. I’m not treating this like a “list of topics.” I’m mapping how the pieces connect end-to-end: prerequisites (math, Python, ML/DL), then how modern LLMs actually work (tokenization, transformers, training vs inference), and finally how you turn models into real products with RAG, agents, and system design. I break the roadmap into clear stages: first get the core ML + deep learning foundations solid, then move into Generative AI and LLM internals. From there, I focus heavily on Retrieval-Augmented Generation—vector databases, retrieval strategies, and evaluation—because that’s where most real-world LLM apps live today. Then I go into Agentic AI: tool usage, memory, reasoning loops, and multi-agent setups. I wrap it with AI system design and MLOps patterns—deployment, scalability, and staying current with research—so you don’t just “learn GenAI,” you become future-ready to build and iterate fast. I also share my GitHub roadmap doc with resources, papers, and project ideas you can follow step-by-step.