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Core
Prompt and Semantic Caching
Caching is one of the cheapest, highest-impact LLM optimizations. Prefix (prompt) caching reuses the computed attention state for a shared prompt prefix (a long system prompt or document), cutting prefill cost and latency. Semantic caching serves a stored answer for a query that is similar (not identical) to a past one, by embedding the query and matching nearest neighbors. Applied-AI interviews probe it because repetitive traffic is everywhere, and caching turns expensive recomputation into near-free lookups, with a correctness caveat for semantic caching.
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PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsYou are adding image support to your assistant. What happens to your p99 latency and your bill?→System Design for AI in ProductionSize the GPU fleet for an internal LLM assistant: 2,000 employees, 8K context. How many cards, and should you self-host at all?→MLOps & ML EngineeringHow do prompt caching and semantic caching cut LLM cost and latency, and what are the risks?→LLM & GenAI FundamentalsYour LLM's answers are too long and rambling. How do you control response length in production?→System Design for AI in ProductionDesign a semantic cache for LLM responses that cuts cost and latency without serving stale or wrong answers.→ML Infrastructure & GPUsServe a 70B-parameter model with high throughput. Do the memory math and name the optimizations.→
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