Understand it.
Then explain it.
First-principles guides to the AI concepts that come up in serious technical interviews—built with diagrams, code, and numbers you can reason through.
The study library
5 topics
RAG
How a model finds useful evidence before it answers—and where retrieval systems quietly fail.
Attention
The query–key–value mechanism, worked by hand before we touch a single line of PyTorch.
RLHF
How human preferences become a training signal—from pairwise labels to a safer policy.
KV Cache
Why autoregressive generation would be painfully wasteful without cached keys and values.
Evals
How to measure whether an LLM system is actually getting better—without gaming your test.
Learn the mechanism,
not the buzzword.
Every guide starts with the smallest useful mental model, makes the arithmetic visible, then climbs toward production trade-offs. Interview prompts are framed as practice—not leaked question banks—so you learn to reason under follow-up, not memorize a script.