A feature that lets an AI provider reuse a long, repeated block of instructions or reference text instead of reprocessing it each time. For agencies, it cuts the cost and delay of workflows built on big brand guidelines.
Also known as context caching
Prompt caching stores the processed form of a long, unchanging part of a prompt, such as a style guide, a set of rules or a reference document. When the next request begins with the same block, the provider reads it from the cache instead of computing it again.
Cached input is typically billed at a fraction of the normal rate and returns faster. The saving applies only to the repeated, unchanged portion at the start of the prompt, and caches expire after a short time if unused.
Agency AI workflows tend to send the same huge instructions over and over, which is exactly what caching rewards.
Brand guidelines become cheap to reuse. A 40-page voice-and-tone document attached to every request costs far less when it is cached.
Speed improves. Long prompts respond faster on a cache hit, which matters in tools that creatives use live.
Prompt design affects the bill. Putting stable content first and variable content last is what makes a cache hit possible, so workflow structure becomes a cost decision.
An agency runs a tool that drafts social captions for a fashion client, and every request carries the 30-page brand voice guide, the banned-words list and 20 approved sample posts. Before caching, each batch of 200 captions reprocessed all of it 200 times. After restructuring the prompt so that the guide comes first and the single product brief comes last, most requests hit the cache and the batch runs noticeably faster and cheaper. The team now reviews prompt order whenever a new client workflow is built.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.