LLMInference & InterfacesUpdated 2026.04.29

Prompt Caching

Also known as프롬프트 캐시Context CachingKV Cache 재사용

In one line

Prompt caching reuses the computation done on a repeated system prompt or document so subsequent calls are dramatically cheaper and faster — a direct lever on operating costs for repetitive workloads like GEO monitoring.

Going deeper

Prompt caching saves the internal state computed for a repeated prompt — system prompt, fixed documents, long context — so that subsequent calls do not have to recompute it. OpenAI, Anthropic and Google each offer their own flavour. On cache hit, input token cost typically drops by 50–90% and time-to-first-token shrinks meaningfully.

For marketers it is a direct lever on AI operating costs. Workloads with heavy repetition — daily GEO monitoring runs that fire the same system prompt hundreds of times, in-house RAG that reuses the same document set every call — are exactly the cases where caching pays off.

Practical caveat: caches usually require the leading portion of the prompt to match byte-for-byte. The standard template is 'system prompt → fixed reference docs → variable user input', in that order, to maximise hit rate.

Sources

Related terms

LLM

System Prompt

A system prompt is the instruction sent to an LLM before any user message, defining the assistant's role, tone and rules — effectively the AI product's character.

LLM

Context Window

The context window is the maximum number of tokens an LLM can take in at once — it defines how much content the model can consider in a single prompt.

LLM

RAG

RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.

LLM

Context Engineering

Context engineering goes beyond crafting a single prompt — it is the design discipline of deciding which context to assemble and how to feed it to the model, an idea that crystallised in 2024–2025.

LLM

A large language model (LLM) is a neural network trained on massive text corpora to understand and generate human language — the engine behind ChatGPT, Claude, Gemini and similar products.

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