Deep explanation
Implement Anthropic correctly in a real-time analytics copilot
Junior implementation question: ship Claude in an analytics copilot in two weeks using the Messages API, tool use for live queries, streaming, prompt caching, and safe SQL execution, with a clear test plan.
Quick answer
I would use Claude's Messages API with tool use: Claude decides which metric query to run, our backend runs it with a read-only, time-limited connection, and Claude explains the numbers it got back. I'd stream the answer so users see text quickly, handle rate-limit and overload errors with retries, and keep our own interface so we could swap models later. Before launch I'd test on a set of real questions with known answers and check that every number in the reply came from a tool result.
The interview question
You have two weeks to add an Anthropic Claude model to a real-time analytics copilot, where users ask questions like "why did signups drop in the last hour?" and the copilot queries live metrics and explains the result. What would you build, which Claude API features would you use, and how would you validate it before launch?
Deep explanation
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