Deep explanation
Implement Cost Reduction correctly in a real-time analytics copilot
Junior implementation question: reduce LLM cost in a real-time analytics copilot in two weeks by measuring token usage, trimming schema context, routing models, prompt caching, and validating with an execution-accuracy test set.
Quick answer
First I measure where the money goes: tokens per request split into system prompt, schema context, conversation history, and output, plus how many LLM calls one question makes. The usual quick wins are sending only the relevant tables instead of the whole schema, capping history and output length, using a cheaper model for simple steps, and using the provider's prompt caching for the fixed part of the prompt. I validate every change on a fixed set of real questions by checking that the SQL still returns the correct results, then roll out behind a flag while watching cost and accuracy.
The interview question
Your manager asks you, as a junior engineer, to cut the LLM cost of a real-time analytics copilot within two weeks without making answers worse. The copilot turns user questions into SQL over live metrics and explains the results. What would you measure first, which changes would you implement, and how would you validate that nothing broke?
Deep explanation
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