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Prompt Caching and Semantic Caching

Two different caches solve two different bills. Prompt caching reuses the model's internal computation over a stable prefix, cutting cost and time-to-first-token on every call that shares it. Semantic caching skips the model entirely when a near-identical question has been answered before, and it is the one that can serve a wrong answer confidently.

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RELATED CONCEPTS

KV Cache AI Cost and Unit Economics Latency Optimization Model Routing and Cascades Embeddings & Vector Representations

LESSONS THAT TEACH THIS

The FDE EngagementCost per unit of their work is the number, and it decides the architecture14 min→Hard DeploymentsServe it from cache, send less, or send it somewhere cheaper18 min→

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsWhat is a token, and why should an FDE care about tokenization?→System Design & Production EngineeringWhat do p50, p95, and p99 latency actually mean, and why do production engineers obsess over the tail?→System Design & Production EngineeringEstimate the capacity and cost of an app with 50k DAU making 10 LLM calls each. What do you provision for?→System Design & Production EngineeringIt's 9am Monday and p99 latency is 10x normal. Walk me through your first 30 minutes.→Behavioral & Customer ScenariosEstimate: how many LLM tokens per day would a Fortune-500 customer-support org consume?→ML System Design (Product)Build a fraud-detection model for payments.→