How do you optimize a RAG or agent system for cost and latency in production?
RAG and agents turn expensive and slow quickly: retrieval plus reranking plus big-model calls, multiplied across agent steps. The signal is naming the dominant cost first, then the levers that genuinely move it. Here is the playbook.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
RAG and agents turn expensive and slow quickly: retrieval plus reranking plus big-model calls, multiplied across agent steps. The signal is naming the dominant cost first, then the levers that genuinely move it. Here is the playbook.
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.
