How to learn AI Engineering in 2026 β the real curriculum
Most 'AI engineering' bootcamps stop at 'call the OpenAI API'. The actual job is closer to data engineering than chatbot building. Here's what to study, in order.
AI engineering in 2026 isn't 'use ChatGPT in your app.' It's: model selection by cost/latency, prompt caching with TTL math, RAG with hybrid BM25 + dense retrieval + reranking, evaluation harnesses with bootstrap confidence intervals, structured-output failure recovery, agent loops with checkpoint resumption, fine-tuning trade-offs, deployment with observability. Most learning material on Google is six months behind on at least three of these.
Our AI Engineering track is 101 lessons designed by someone who's actually built this. Modules: Anthropic SDK fundamentals, prompt caching ($5/M β $0.30/M after cache), tool use with retries, RAG end-to-end (chunking strategies, embedding choice, reranking), evals (golden dataset, fail-on-regression, drift detection), agent loops (planner-worker-synthesizer), production deployment (rate limits, fallback chains, cost monitoring). Capstone: a production RAG system with full eval harness. Companion 90-day path schedules it into 30-min daily blocks.