ai-system-design-guide

Why we included this project

Engineers prepping for machine-learning system design interviews will get more from this than a deck of memorized answers, because it is built around the decisions that actually come up when shipping production AI: how to frame a problem, when to pick retrieval-augmented generation over fine-tuning, what an evaluation pipeline should contain, and how agentic workflows behave across cloud platforms. Chapters cross-link to one another, and the companion reader at aidaddy.tech adds search, which makes it fast to jump to a specific topic during interview prep or while roughing out a new service. Since it keeps returning to the same trade-offs and concrete patterns, it also reads well as a refresher for a team revisiting its own architecture choices. The author keeps it updated with new model releases, so it works as a current reference rather than a snapshot.