caching
Applied AI interview questions tagged caching, across every topic.
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Concepts behind "caching"
The curriculum that explains the ideas these questions test.
Foundational
The LLM GatewayAn LLM gateway is a single proxy layer between your application and one or more model providers. It centralizes the cross-cutting concerns every LLM app needs: routing and fallback across models/providers, caching, rate limiting, authentication, cost tracking, observability, and guardrails. It also prevents vendor lock-in by abstracting providers behind one interface. Applied-AI interviews probe it because it is the backbone of a production LLM platform and the place most operational controls live.⚙️ System Design for AI in Production
Foundational
LLM Cost OptimizationLLM systems get expensive fast, and the cost model is mostly tokens and number of model calls. The levers, in rough order of impact: route easy queries to cheaper/smaller models, cache repeated and similar requests, trim context (fewer, better chunks), use cheaper retrieval/reranking, and for agents cut unnecessary steps. The discipline is measuring cost per request and attacking the dominant contributor. Applied-AI interviews probe it because cost is a primary production constraint and most teams overspend by defaulting to the biggest model on everything.⚙️ System Design for AI in Production
Foundational
Caching StrategiesA cache trades freshness for speed by keeping a copy of hot data closer to the request. The strategy is the write/read pattern: cache-aside (app fills the cache on a miss), write-through (writes go through the cache to the store), write-back (writes hit the cache and flush later). Eviction (LRU, LFU) and TTL decide what to keep, and cache stampede protection stops a popular expired key from hammering the backing store. CDNs are caches at the network edge. Applied-AI interviews probe it because LLM responses, embeddings, and retrieval results are expensive enough that caching is a first-class design decision.⚙️ System Design for AI in Production