The prompt itself is your biggest cost line. How do you optimize it without losing quality?
Every request pays for the prompt and almost nobody has looked at where the tokens actually go. It is rarely the part you can read: thirty tool schemas and seven stale examples usually outweigh the system prompt, and the output side costs several times more per token.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Every request pays for the prompt and almost nobody has looked at where the tokens actually go. It is rarely the part you can read: thirty tool schemas and seven stale examples usually outweigh the system prompt, and the output side costs several times more per token.
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.
