How do you build RAG over a SQL database (text-to-SQL) when the answer lives in rows, not documents?
Vector search over rows is the wrong tool when the user asks for a count or an aggregate. Interviewers want to see you retrieve the right schema, generate validated SQL, and know when to query the database instead of embedding it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Vector search over rows is the wrong tool when the user asks for a count or an aggregate. Interviewers want to see you retrieve the right schema, generate validated SQL, and know when to query the database instead of embedding it.
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.
