Prompt Caching
Practice of storing previously used prompts and their corresponding AI-generated outputs to improve efficiency and reduce computational costs in AI systems.
In AI systems, especially those utilizing large language models (LLMs) like GPT, generating responses to prompts can be resource-intensive due to the need for significant computational power. Prompt caching optimizes this process by storing the inputs (prompts) and their outputs after the initial processing. When a similar or identical prompt is encountered later, the system can retrieve the cached response instead of recomputing it. This not only speeds up the response time but also reduces the load on the processing infrastructure. Prompt caching is particularly useful in scenarios where certain prompts are frequently repeated or where a consistent response is needed across multiple queries.
The concept of caching itself dates back to the early days of computing, with memory caching being a fundamental technique used to enhance performance. Prompt caching in AI, however, gained attention with the advent of large-scale language models in the 2020s, as these models required innovative methods to manage the intensive computation and resources they demand.
Prompt caching as a specific concept doesn't have a single inventor but emerged from broader caching practices in computing. Researchers and engineers working on optimizing large-scale AI systems, particularly within companies like OpenAI, Google, and Microsoft, have contributed to refining and popularizing this approach to enhance the efficiency of LLMs.