Prompt caching represents a critical optimization for scaling Large Language Model (LLM) applications, addressing the persistent challenges of high costs and latency as request volumes grow. In complex AI agents and Retrieval Augmented Generation (RAG) systems, a single user query often triggers multiple LLM calls, making efficiency paramount. Fortunately, a significant portion of input tokens in these applications is repetitive, consisting of system instructions, fixed guidelines, or shared context that appears across numerous requests. Caching addresses this by storing frequently used data to serve future requests faster. The concept relies on the Pareto principle, where roughly 80 percent of requests involve 20 percent of the data. By identifying and reusing these common elements, applications can drastically reduce operational expenses. According to OpenAI documentation, prompt caching can lower latency by up to 80 percent and reduce input token costs by 90 percent. To understand prompt caching, one must distinguish it from standard KV (Key-Value) caching. While KV caching optimizes the generation of a single response by storing intermediate calculations for previous tokens within a session, prompt caching extends this efficiency across different prompts and users. It focuses on the prompt prefix—the static part of the input such as system instructions. When a new request begins with the same tokens as a previously processed prompt, the system utilizes the precomputed calculations for that shared prefix rather than recalculating them from scratch. This means the model only performs new calculations for the varying parts of the query, such as the specific user question or dynamic data appended at the end. The structure of the prompt is therefore vital. For caching to function effectively, static information must be placed at the very beginning of the input. If the first tokens differ, even slightly, the system registers a cache miss. Consequently, best practices dictate that variable elements like timestamps or user identifiers should be moved to the end of the prompt, ensuring the shared prefix remains constant. Major AI providers like OpenAI have integrated this feature directly into their APIs. Caching is typically enabled by default on recent models and operates across all users within an organization sharing the same API key. This collective benefit is particularly valuable for enterprise applications with high concurrent traffic. However, there are practical constraints. OpenAI requires a minimum of 1,024 tokens in the prefix to activate caching, and cached data is retained for a maximum of 24 hours. These thresholds mean the most significant cost reductions are realized in large-scale deployments where thousands of requests are processed daily. In a practical demonstration, an application using a large shared prefix of 19,840 tokens saw a dramatic reduction in billing when processing a second, similar query. While the first request processed the entire text, the second request was billed only for the 174 non-identical tokens, achieving a 99 percent reduction in token usage for that specific interaction. As AI systems continue to scale, prompt caching is becoming an essential tool for maintaining affordability and speed. By eliminating redundant computation for identical prompt prefixes, developers can build more responsive and cost-effective AI agents without sacrificing performance.