Welcome to Generative AI Engineering

Phase 1 gave you the vocabulary underneath every model you will ever touch: what a loss function is, why gradient descent works, what overfitting looks like, how a neural network turns numbers into predictions. That vocabulary was deliberately general. It applies whether the model classifies spam, predicts churn, or writes code. Phase 2 narrows the focus onto one specific, extremely well-paid, and rapidly growing specialization built on top of all of it: generative AI engineering, the job of building real products powered by large language models.

Here is the distinction that trips up almost everyone new to this field. Training GPT-4 or Claude from scratch, choosing an architecture, running a months-long pretraining job on thousands of GPUs, is the work of a tiny number of research labs. That is not what this phase teaches, and it is not what the overwhelming majority of "AI engineer" job postings are actually asking for. GenAI engineering is the discipline of building systems on top of an already-trained foundation model: prompting it reliably, retrieving the right information to ground its answers, giving it tools so it can act instead of just talk, and running all of that in production without it becoming slow, expensive, or unsafe.

• -A support chatbot that answers from your company's actual documentation, not the model's general training data.
• -A coding assistant that reads your specific codebase and suggests changes that actually compile.
• -A research agent that plans a multi-step task, calls search and calculator tools, and checks its own work.
• -A content moderation pipeline that classifies and filters at the scale of millions of posts a day.
• -An internal tool that turns a support ticket, a calendar, and a CRM into one assistant that can actually do things, not just describe them.

Every one of those systems uses the same handful of building blocks, prompts, retrieval, tools, memory, evaluation, guardrails, combined in different proportions. Learning those building blocks once, deeply, is what the rest of this phase is for.

• -LLM Foundations (this tier): how a model actually sees text, represents meaning, remembers a conversation, and can be steered through prompting and sampling settings.
• -Retrieval-Augmented Generation (RAG): grounding a model's answers in your own documents instead of trusting its memorized training data.
• -Agents & Tool Use: giving a model the ability to call functions, use tools, and complete multi-step tasks instead of just producing text.
• -Production & LLMOps: serving, caching, fine-tuning, guardrails, cost control, and observability, the parts that separate a demo from a product.
• -Capstone: full worked system designs, the exact shape of a real GenAI system design interview.