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.
If you cannot explain attention from first principles, you cannot reliably debug transformer behavior, tune serving latency, choose model architecture, or defend tradeoffs in interviews. Modern GenAI roles expect both theory fluency and production intuition.
Many GenAI outages and budget overruns are token engineering failures: tokenizer mismatch, context over-packing, and missing token guardrails. Teams that treat token budget as a core systems resource ship faster and cheaper with fewer regressions.
Question banks with layered answers and follow-up ladders.
This file prepares deep technical interviews on transformer internals, tokenization behavior, and production tradeoffs.