update spotlighting copy

Author: PatrickFarley

articles/ai-foundry/openai/concepts/content-filter-prompt-shields.md

@@ -19,9 +19,10 @@ Prompt shields analyzes LLM inputs and detects adversarial user input attacks.
 
 ## Spotlighting for prompt shields
 
-Spotlighting is a sub-feature of prompt shields that enhances protection against indirect attacks by tagging the input documents with special formatting to indicate lower trust to the model. When spotlighting is enabled, the service transforms the document content using base-64 encoding, and the model is configured to treat this content as less trustworthy than direct user and system prompts. This helps prevent the model from executing unintended commands or actions that are found in the content of the documents.
+Spotlighting is a feature that enhances protection against indirect attacks by tagging the input documents with special formatting to indicate lower trust to the model. When spotlighting is enabled, the service transforms the document content using base-64 encoding, and the model treats this content as less trustworthy than direct user and system prompts. This helps prevent the model from executing unintended commands or actions that are found in the content of the documents.
 
-Spotlighting is turned off by default, and users can enable it when [configuring their content filter](../how-to/content-filters.md) in the Azure AI Foundry portal or REST API.
+Spotlighting is turned off by default, and users can enable it when [configuring their content filter](../how-to/content-filters.md) in the Azure AI Foundry portal or REST API. Spotlighting is only available for [models in the Chat Completions API](/azure/ai-foundry/openai/how-to/chatgpt).
 
-There is no direct cost for spotlighting, but it adds more tokens to a user's prompt, which could increase the total costs. Also note that spotlighting could make a lengthy document surpass the input size limit. 
+There is no direct cost for spotlighting, but it adds more tokens to a user's prompt and to the system prompt, which could increase the total costs. Also note that spotlighting could make a lengthy document surpass the input size limit.
 
+An occasional known side effect of spotlighting is the model response mentioning the fact that the document content was base-64 encoded, even when neither the user nor the system prompt asked about encodings.