AI Engineer Interview Daily — 2026-09-03: LLM & Agent Engineering
LLM & Agent Engineering interviews don't test whether you can wire up LangChain — they test whether you can turn a vague 'something's broken' into a set of testable sub-hypotheses. Today covers four high-frequency concepts: why RAG's 'retrieval is right but the answer is wrong' gap needs retrieval and generation evaluated as two separate systems; why a bigger agent context window doesn't fix quality decay, because the real problem is context pollution (noise diluting attention) rather than too few tokens; the decision logic between RAG and fine-tuning — 'RAG owns knowledge, fine-tuning owns behavior'; and why LLM-as-judge carries consistency and self-preference bias and can't stand alone as the only metric. The practice problem is a common enterprise scenario question — retrieval accuracy is 90% but answer accuracy is only 60%, how do you diagnose it — walking through how to turn a vague symptom into verifiable hypotheses.