Free PDF · 12 pages · October 2026
AI Engineering Cheat Sheet
What our labs measured about tokens, prompting, RAG, evals, agents, serving, cost and fine-tuning, and the rule each result teaches.
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Each result comes from a lab in the AI Engineering course, with the model it ran on. Every lesson is linked, so you can read how it was measured.
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What is inside
- 01Tokens and embeddings, and prompting that holds up
- 02RAG: chunk size and overlap measured, embeddings, HNSW, hybrid search and reranking
- 03Evals: error analysis first, and the ways an LLM judge misleads you
- 04Agents in production, serving and inference, and where the cost goes
- 05Fine-tuning: when it pays, and why production breaks
Every line comes from a lesson. These are the courses.
course 1
System Design Masterclass
770 lessons
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Every lesson in both courses has an AI tutor beside it. It reads the same lesson you are reading and answers your questions from it. Try the tutor