As AI applications scale, developers face a critical challenge: how to handle the growing cost and latency of repeated LLM calls. Prompt Caching emerges as a powerful solution, with OpenAI reporting up to 80% latency reduction and 90% input token cost savings when implemented correctly.

Caching isn't new to computing - it's the same principle that makes your web browser load frequently visited pages instantly. When you revisit a site, your browser checks its local storage first, retrieving data from cache rather than making expensive network requests. This concept applies directly to LLM interactions, where the same input tokens appear across multiple requests.

Large language models process text through two stages: pre-fill (computing the initial context) and decoding (generating tokens one by one). Without caching, models redundantly process the same tokens repeatedly during decoding. KV caching stores intermediate calculations, but Prompt Caching takes this further by reusing computations across different prompts, users, and sessions. This means shared token prefixes - like system instructions or common questions - are processed once and retrieved instantly thereafter, making AI applications significantly more efficient.