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What AI engineering actually is, how it differs from ML engineering and software engineering, the three-layer AI stack, and the most important mindset shift: LLMs are not functions.

What post-training (RLHF, DPO) actually does to model behavior. How sampling and temperature work. Structured outputs. Why the same prompt returns different answers — and what that means for how you build.

Why evaluation is the hardest problem in AI engineering. Three methods: exact match, AI-as-judge, and comparative evaluation. How to build an eval pipeline. How to select a model using your own data — not public benchmarks.

System vs user prompt architecture. In-context learning: zero-shot vs few-shot. The context window as a constrained resource, not free space. Defensive prompt engineering: injection, jailbreaking, and information extraction.

When to use RAG, finetuning, or just prompting. RAG architecture and what makes retrieval fail. Why most finetunes don't work — and the three conditions that justify trying. PEFT and LoRA as the practical entry point.

The production pipeline: input guardrails → context enhancement → model routing → generation → output validation → caching. Monitoring, observability, and how to close the user feedback loop.