wip

Author: gitName

articles/api-management/genai-gateway-capabilities.md

@@ -7,7 +7,7 @@ author: dlepow
 ms.service: azure-api-management
 ms.collection: ce-skilling-ai-copilot
 ms.topic: concept-article
-ms.date: 02/05/2025
+ms.date: 04/29/2025
 ms.author: danlep
 ---
 
@@ -18,7 +18,7 @@ ms.author: danlep
 This article introduces capabilities in Azure API Management to help you manage generative AI APIs, such as those provided by [Azure OpenAI Service](/azure/ai-services/openai/overview). Azure API Management provides a range of policies, metrics, and other features to enhance security, performance, and reliability for the APIs serving your intelligent apps. Collectively, these features are called *AI gateway capabilities* for your generative AI APIs.
 
 > [!NOTE]
-> * This article focuses on capabilities to manage APIs exposed by Azure OpenAI Service. Many of the AI gateway capabilities apply to other large language model (LLM) APIs, including those available through [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api).
+> * Use AI gateway capabilities to manage APIs exposed by Azure OpenAI Service and with other large language model (LLM) APIs, including those available through [Azure AI Model Inference API](/azure/ai-studio/reference/reference-model-inference-api) or with OpenAI-compatible models served through third-party inference providers.
 > * AI gateway capabilities are features of API Management's existing API gateway, not a separate API gateway. For more information on API Management, see [Azure API Management overview](api-management-key-concepts.md).
 
 ## Challenges in managing generative AI APIs
@@ -57,7 +57,7 @@ The following basic example demonstrates how to set a TPM limit of 500 per subsc
 ```
 
 > [!TIP]
-> To manage and enforce token limits for LLM APIs available through the Azure AI Model Inference API, API Management provides the equivalent [llm-token-limit](llm-token-limit-policy.md) policy.
+> To manage and enforce token limits for other LLM APIs, API Management provides the equivalent [llm-token-limit](llm-token-limit-policy.md) policy.
 
 
 ## Emit token metric policy 
@@ -79,7 +79,7 @@ For example, the following policy sends metrics to Application Insights split by
 ```
 
 > [!TIP]
-> To send metrics for LLM APIs available through the Azure AI Model Inference API, API Management provides the equivalent [llm-emit-token-metric](llm-emit-token-metric-policy.md) policy.
+> To send metrics for other LLM APIs, API Management provides the equivalent [llm-emit-token-metric](llm-emit-token-metric-policy.md) policy.
 
 ## Backend load balancer and circuit breaker
 
@@ -102,7 +102,11 @@ Configure [Azure OpenAI semantic caching](azure-openai-enable-semantic-caching.m
 In API Management, enable semantic caching by using Azure Redis Enterprise or another [external cache](api-management-howto-cache-external.md) compatible with RediSearch and onboarded to Azure API Management. By using the Azure OpenAI Service Embeddings API, the [azure-openai-semantic-cache-store](azure-openai-semantic-cache-store-policy.md) and [azure-openai-semantic-cache-lookup](azure-openai-semantic-cache-lookup-policy.md) policies store and retrieve semantically similar prompt completions from the cache. This approach ensures completions reuse, resulting in reduced token consumption and improved response performance. 
 
 > [!TIP]
-> To enable semantic caching for LLM APIs available through the Azure AI Model Inference API, API Management provides the equivalent [llm-semantic-cache-store-policy](llm-semantic-cache-store-policy.md) and [llm-semantic-cache-lookup-policy](llm-semantic-cache-lookup-policy.md) policies.
+> To enable semantic caching for other LLM APIs, API Management provides the equivalent [llm-semantic-cache-store-policy](llm-semantic-cache-store-policy.md) and [llm-semantic-cache-lookup-policy](llm-semantic-cache-lookup-policy.md) policies.
+
+
+## Content safety policy
+
 
 
 ## Labs and samples