Top 4 Prompt caching Interview Questions
Browse 4 real-world technical and behavioral interview questions about Prompt caching. Review scenarios, edge cases, and architectural best practices.
You serve millions of prompts an hour and the LLM bill doubled. Where does the money go, and what do you cut?
Split LLM cost by feature and by line: uncached input, cached input, output, retries and hidden callers. Find which line moved before cutting anything. A collapsed prompt-cache hit rate or longer replies can double a bill with no change in traffic; fix those before you downgrade a model.
Users say the LLM feature in your product feels slow. How do you work out what to fix?
Split the measurement into time-to-first-token and inter-token latency, because they come from different phases. The first is driven by prompt length and queueing and is fixed with shorter prompts, prefix caching and streaming; the second is the decode loop, fixed only by generating fewer tokens.
Before we build this, how do you estimate what the LLM feature will cost to run?
Write down requests per day, input tokens per request and expected output tokens, multiply by the provider's per-million rates, then test each assumption. Output tokens cost several times input, retries and agent loops multiply the input, and a cached prefix rewrites the arithmetic.
What belongs in the system prompt, and what belongs in the user turn?
The system prompt holds what is identical on every request - role, policy, output contract, tool schemas, examples. Anything varying by user or request, retrieved documents included, belongs in the turns below, to keep the cached prefix stable and untrusted text away from system authority. It also connects prompt caching to the point an interviewer is testing.