2 explainers and 1 interview packs. Track your reading and drill this module end-to-end before moving ahead.
Concept-first deep dives with practical implementation context.
In modern GenAI stacks, every meaningful change can shift quality, cost, and safety at the same time. Teams that ship safely are eval-driven teams: they define expected behavior, measure it continuously, and gate deployment with explicit risk policies.
Teams often over-apply autonomous agents where deterministic workflows are faster, safer, and cheaper. Strong GenAI engineers are judged on architecture restraint: choosing the minimum autonomy needed to meet quality goals.
Question banks with layered answers and follow-up ladders.
This file prepares advanced interviews on workflow versus agent decisions, reliability controls, and safety governance under production constraints.