Prompt caching

Prompt Caching: Key Concepts

• Definition and Purpose:
• Prompt caching is a feature designed to speed up the response time of language models (like Claude) and decrease the cost of text generation.
• It involves saving the internal work performed during an initial request so it can be reused in subsequent requests.

• Normal Request Flow (Without Caching):
• When a message is sent, the model performs extensive internal work:
• It creates numerous internal data structures.
• It executes complex calculations solely on the input text.
• After generating the output, the model discards all the internal calculations and data structures (the work is "thrown away").

• The Problem (Follow-up Requests):
• In a conversation, follow-up requests often include the original input message.
• Without caching, the model sees the original message again and must repeat all the initial, time-consuming analysis, even though it already performed that work moments before.
• This repetition slows down the process and increases computational load.

• The Solution (Prompt Caching):
• Instead of discarding the internal analysis after the initial request, the model saves (caches) that work in a temporary data store.
• When a follow-up request contains the exact same input message, the model retrieves the saved work from the cache.
• The model reuses the previously completed analysis, dramatically speeding up the generation of the new response.

Takeaways

• Prompt caching speeds up response time and decreases generation cost by saving the internal work performed during an initial request.
• Without caching, models discard internal calculations after generating output, forcing them to repeat complex analysis on the same input in follow-up requests.
• The caching solution saves the initial analysis in a temporary data store instead of discarding it.
• When a follow-up request matches the original input, the model reuses the saved work, dramatically speeding up the response generation.