How do you evaluate the retrieval component of a RAG system (separately from generation)?
RAG failures are usually retrieval failures, yet most teams measure only the final answer. The signal is scoring retrieval on its own (recall@k, precision@k, MRR/nDCG) to pin down exactly where the system breaks.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
RAG failures are usually retrieval failures, yet most teams measure only the final answer. The signal is scoring retrieval on its own (recall@k, precision@k, MRR/nDCG) to pin down exactly where the system breaks.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
