Update prompt-caching.md

Author: JillGrant615

articles/ai-foundry/openai/how-to/prompt-caching.md

@@ -14,20 +14,20 @@ recommendations: false
 
 # Prompt caching
 
-Prompt caching allows you to reduce overall request latency and cost for longer prompts that have identical content at the beginning of the prompt. *"Prompt"* in this context is referring to the input you send to the model as part of your chat completions request. Rather than reprocess the same input tokens over and over again, the service is able to retain a temporary cache of processed input token computations to improve overall performance. Prompt caching has no impact on the output content returned in the model response beyond a reduction in latency and cost. For supported models, cached tokens are billed at a [discount on input token pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) for Standard deployment types and up to [100% discount on input tokens](/azure/ai-services/openai/concepts/provisioned-throughput) for Provisioned deployment types. If you provide the `user` parameter, it is combined with a prefix hash, allowing you to influence routing and improve cache hit rates. This is especially beneficial when many requests share long, common prefixes.
+Prompt caching allows you to reduce overall request latency and cost for longer prompts that have identical content at the beginning of the prompt. *"Prompt"* in this context is referring to the input you send to the model as part of your chat completions request. Rather than reprocess the same input tokens over and over again, the service is able to retain a temporary cache of processed input token computations to improve overall performance. Prompt caching has no impact on the output content returned in the model response beyond a reduction in latency and cost. For supported models, cached tokens are billed at a [discount on input token pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) for Standard deployment types and up to [100% discount on input tokens](/azure/ai-services/openai/concepts/provisioned-throughput) for Provisioned deployment types. If you provide the `user` parameter, it's combined with a prefix hash, allowing you to influence routing and improve cache hit rates. This is especially beneficial when many requests share long, common prefixes.
 
-Caches are typically cleared within 5-10 minutes of inactivity and are always removed within one hour of the cache's last use. Prompt caches are not shared between Azure subscriptions.
+Caches are typically cleared within 5-10 minutes of inactivity and are always removed within one hour of the cache's last use. Prompt caches aren't shared between Azure subscriptions.
 
 ## Supported models
 
 - Prompt caching is supported with all Azure OpenAI models GPT-4o or newer.
-- Prompt caching applies to models that have chat-completion, completion, responses, or real-time operations. For models which do not have these operations, this feature is not available.
+- Prompt caching applies to models that have chat-completion, completion, responses, or real-time operations. For models which don't have these operations, this feature isn't available.
 
 ## API support
 
 Official support for prompt caching was first added in API version `2024-10-01-preview`. At this time, only the o-series model family supports the `cached_tokens` API response parameter.
 
-## Getting started
+## Get started
 
 For a request to take advantage of prompt caching the request must be both:
 
@@ -80,4 +80,4 @@ To improve the likelihood of cache hits occurring, you should structure your req
 
 ## Can I disable prompt caching?
 
-Prompt caching is enabled by default for all supported models. There is no opt-out support for prompt caching.
+Prompt caching is enabled by default for all supported models. There's no opt-out support for prompt caching.