In this video, I lay out a focused 8–10 week plan to build 9 end-to-end GenAI projects before 2025—so your GitHub shows real system design, not just toy notebooks. I’m covering three buckets that actually matter in the real world: RAG (retrieval-augmented generation), agentic AI (multi-step workflows with tools), and LLM fine-tuning/optimization. For every project, I walk through the full system design, the stack, how I’d deploy it, what you should learn from it, and the optional extensions that make it feel production-ready. On the RAG side, I break down advanced multi-source RAG, “chat with your codebase,” and a multimodal product assistant—because retrieval isn’t just “vector DB + prompt,” it’s ingestion, chunking, metadata, reranking, evaluation, and latency/cost trade-offs. For agents, I include a financial analysis agent crew, a self-correcting data validation agent, and an AI test case generator—projects that force you to think about tool calling, state, retries, observability, and guardrails. And for LLM projects, we go into a fine-tuned domain chatbot, a guardrail moderation API, and a real-time video summarizer—so you practice model adaptation, safety, and streaming pipelines. If you want a portfolio that signals “I can ship GenAI systems,” this is the roadmap.