--- title: AI Interview Questions #3 - Prompt Caching with LLMs description: A mock interview-style discussion covering how to implement caching for LLM-based applications. Covers two main approaches: exact-match caching using a... canonical: https://daily.dev/posts/ai-interview-questions-3---prompt-caching-with-llms-4zsbjbg8j twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: AI Interview Questions #3 - Prompt Caching with LLMs | daily.dev og:description: A mock interview-style discussion covering how to implement caching for LLM-based applications. Covers two main approaches: exact-match caching using a... og:url: https://daily.dev/posts/ai-interview-questions-3---prompt-caching-with-llms-4zsbjbg8j og:image: https://api.daily.dev/og/posts/4zSbjbg8J.png og:image:alt: AI Interview Questions #3 - Prompt Caching with LLMs og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # AI Interview Questions #3 - Prompt Caching with LLMs **[Gaurav Sen](https://daily.dev/sources/gkcs)** · 6 min read · 1 upvotes · 0 comments ## Summary A mock interview-style discussion covering how to implement caching for LLM-based applications. Covers two main approaches: exact-match caching using a key-value store (with TTL or LRU eviction for stale responses), and semantic caching using vector embeddings and approximate nearest neighbor search. Explains trade-offs including increased latency from vector search, potential for approximate/incorrect matches due to embedding information loss, and cost comparisons between embedding model calls versus full LLM inference calls. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How does semantic caching differ from exact-match caching for LLM queries? Exact-match caching uses a key-value store where the user query text must match a cached key precisely, similar to a hashmap lookup. Semantic caching instead converts queries into vector embeddings and searches for the nearest vector using approximate nearest neighbor algorithms, treating queries above a similarity threshold (commonly cosine similarity above 0.9) as equivalent even when wording differs. _Comparing caching strategies for LLM apps is easier with daily.dev's technical explainers on hand._ ### What are the downsides of using a semantic cache instead of an exact-match cache for LLM applications? Semantic caching introduces more points of failure: embedding a query loses information, so semantically dissimilar queries like a $3,000 laptop request and a $1,000 laptop request can end up with high similarity scores and produce inaccurate cached answers. It also adds latency because vector search (approximate nearest neighbor) is slower than a simple hashmap key comparison, though the embedding step itself is cheaper than an additional LLM call. _Engineers weighing caching trade-offs for LLM systems can track these patterns via daily.dev._ ### How do you handle stale responses in an LLM prompt cache? Stale responses can be handled either with a timeout, where a cached response is only considered valid for a set duration such as one minute, or with least-recently-used (LRU) eviction, which kicks out older cached prompts as new ones come in. The right approach depends on how frequently the underlying real-world conditions behind a cached answer are likely to change. _Anyone designing cache invalidation for LLM pipelines can keep exploring these ideas on daily.dev._ ## Similar posts on daily.dev - [The Complete Guide to Inference Caching in LLMs](https://daily.dev/posts/the-complete-guide-to-inference-caching-in-llms-uh6o61eic) · Machine Learning Mastery · 1 upvotes · 0 comments - [Implementing Semantic Caching Using Spring AI](https://daily.dev/posts/implementing-semantic-caching-using-spring-ai-anojohlzl) · Baeldung · 4 upvotes · 0 comments - [Why an old caching trick is your secret to lower LLM costs](https://daily.dev/posts/why-an-old-caching-trick-is-your-secret-to-lower-llm-costs-o6xizjbwd) · The New Stack · 0 upvotes · 0 comments - [Why Care About Prompt Caching in LLMs?](https://daily.dev/posts/why-care-about-prompt-caching-in-llms--xsybxgzta) · Towards Data Science · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#nlp](https://daily.dev/tags/nlp), [#vector-search](https://daily.dev/tags/vector-search), [#ai-inference](https://daily.dev/tags/ai-inference) [View this post on daily.dev](https://daily.dev/posts/ai-interview-questions-3---prompt-caching-with-llms-4zsbjbg8j) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"AI Interview Questions #3 - Prompt Caching with LLMs","url":"https://daily.dev/posts/ai-interview-questions-3---prompt-caching-with-llms-4zsbjbg8j","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/ai-interview-questions-3---prompt-caching-with-llms-4zsbjbg8j"},"datePublished":"2026-08-03T19:31:51.111Z","dateModified":"2026-09-13T19:06:08.588Z","description":"A mock interview-style discussion covering how to implement caching for LLM-based applications. 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