Mlops

Large Language Models are not magic. They are statistical syntax engines, massive distributed systems, cost-sensitive GPU workloads, and product infrastructure challenges wrapped in a chat interface. This post breaks down everything that actually matters if you want to move beyond "calling GPT" and become a real AI engineer: architecture, training, tokenization, embeddings, RAG, LoRA, quantization, LLMOps, vector databases, deployment, cost engineering, agents, security, regulation, and the future of production AI systems. If you want to design, operate, and optimize LLM systems at scale - this is your blueprint.