When calling a large-model API, the same system prompt and context get re-sent on every request — a hidden tax. Prompt Caching is changing that.
The technical principle is straightforward. LLM inference has two phases: pre-fill (processing the full prompt to generate the first token, compute-heavy) and decoding (autoregressive token-by-token generation, memory-bandwidth-heavy). When multiple requests share an identical system prompt or base context, caching that portion for reuse eliminates redundant computation.
According to OpenAI's official documentation, Prompt Caching can deliver up to 80% latency reduction and 90% input-token cost cut. Anthropic, Google Gemini, and other major vendors have all added support; the key is how to design your prompt structure to maximize cache-hit rates.
That said, caching strategy is not a silver bullet. The shared system prefix must occupy a fixed position in the prompt, and a cold-start after a cache miss actually carries higher latency. Developers need to strike a balance between cache hit rate and prompt flexibility.
For any AI application that calls LLMs frequently — whether RAG systems, code generation tools, or multi-turn dialogue agents — Prompt Caching is a piece of infrastructure-level optimization worth attention. As model-capability gaps narrow, inference efficiency is becoming the new competitive axis.