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The one-stop companion to the AI Engineering: The Complete Course video — everything in it distilled onto a clean, mobile-friendly set of pages. THE AI ENGINEERING STACK: the 3 layers (Application / Model / Infrastructure) and what lives in each, plus the mindset shift that changes everything — you're a USER of foundation models, not a trainer. THE 10-MODULE MAP: the full course in one glance, one line per module. THE ADAPTATION LADDER (the framework that ties it all together): prompt engineering -> add examples -> basic RAG -> advanced RAG -> finetuning, cheapest first — climb only as far as you have to, and stop jumping straight to finetuning. THE DECISION BOX everyone gets wrong: RAG is for FACTS (the model doesn't KNOW something), finetuning is for FORM (the model doesn't BEHAVE right). THE EVALUATION CHEAT-SHEET: exact methods (functional correctness, similarity) vs AI-as-judge and its 4 biases (self, position, verbosity, inconsistency), the 4 things to actually measure (relevance, factual consistency, safety, quality), and the rule that beats every benchmark — if you care about it, make a test set for it. AGENTS: the 3 tool categories (knowledge / capability / write — and why read-vs-write is the safety line that separates a demo from a disaster) plus the compound-error math (95% per step becomes ~60% over 10 steps and under 1% over 100). INFERENCE LEVERS: prefill vs decode, quantization, speculative decoding, continuous batching, prompt caching. THE ARCHITECTURE LADDER: context -> guardrails -> router+gateway -> caching -> agents -> monitoring, and why the data flywheel — not the model — is your real moat. Plus 4 DO-IT-WITH-CLAUDE recipes: temperature (0 for consistency, up for range), a cheap Claude-as-judge eval harness, minimal RAG ('answer only from the provided context'), and caching + model routing (Haiku for speed, Opus for the hard reasoning). Built on Chip Huyen's 'AI Engineering' (O'Reilly, 2025) — an independent, credited companion to read alongside the book.

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