acrolinx

Author: aahill

articles/ai-foundry/openai/concepts/abuse-monitoring.md

@@ -23,9 +23,9 @@ There are several components to abuse monitoring:
 - **Abuse Pattern Capture**: Azure OpenAI’s abuse monitoring system looks at customer usage patterns and employs algorithms and heuristics to detect and score indicators of potential abuse. Detected patterns consider, for example, the frequency and severity at which harmful content is detected (as indicated in content classifier signals) in a customer’s prompts and completions, as well as the intentionality of the behavior. The trends and urgency of the detected pattern will also affect scoring of potential abuse severity.
     For example, a higher volume of harmful content classified as higher severity, or recurring conduct indicating intentionality (such as recurring jailbreak attempts) are both more likely to receive a high score indicating potential abuse. 
 - **Review and Decision**: Prompts and completions that are flagged through content classification and/or identified as part of a potentially abusive pattern of use are subjected to another review process to help confirm the system’s analysis and inform actioning decisions for abuse monitoring. Such review is conducted through two methods: automated review and human review.
-    - By default, if prompts and completions are flagged through content classification as harmful and/or identified to be part of a potentially abusive pattern of use, they may be sampled for review by using automated means including AI models such as LLMs instead of a human reviewer. The model used for this purpose processes prompts and completions only to confirm the system’s analysis and inform actioning decisions; prompts and completions that undergo such review are not stored by the abuse monitoring system or used to train the AI model or other systems.
-    - In some cases, when automated review does not meet applicable confidence thresholds in complex contexts or if automated review systems are not available, human eyes-on review may be introduced to make an extra judgment. Authorized Microsoft employees may assess content flagged through content classification and/or identified as part of a potentially abusive pattern of use, and either confirm or correct the classification or determination based on predefined guidelines and policies. Such prompts and completions can be accessed for human review only by authorized Microsoft employees via Secure Access Workstations (SAWs) with Just-In-Time (JIT) request approval granted by team managers. For Azure OpenAI resources deployed in the European Economic Area, the authorized Microsoft employees are located in the European Economic Area. This human review abuse monitoring process will not take place if the customer has been approved for modified abuse monitoring. 
-- **Notification and Action**: When a threshold of abusive behavior has been confirmed based on the preceding steps, the customer is informed of the determination by email. Except in cases of severe or recurring abuse, customers typically are given an opportunity to explain or remediate—and implement mechanisms to prevent recurrence of—the abusive behavior. Failure to address the behavior—or recurring or severe abuse—may result in suspension or termination of the customer’s access to Azure OpenAI resources and/or capabilities.
+    - By default, if prompts and completions are flagged through content classification as harmful and/or identified to be part of a potentially abusive pattern of use, they might be sampled for review by using automated means including AI models such as LLMs instead of a human reviewer. The model used for this purpose processes prompts and completions only to confirm the system’s analysis and inform actioning decisions; prompts and completions that undergo such review are not stored by the abuse monitoring system or used to train the AI model or other systems.
+    - In some cases, when automated review does not meet applicable confidence thresholds in complex contexts or if automated review systems are not available, human eyes-on review might be introduced to make an extra judgment. Authorized Microsoft employees may assess content flagged through content classification and/or identified as part of a potentially abusive pattern of use, and either confirm or correct the classification or determination based on predefined guidelines and policies. Such prompts and completions can be accessed for human review only by authorized Microsoft employees via Secure Access Workstations (SAWs) with Just-In-Time (JIT) request approval granted by team managers. For Azure OpenAI resources deployed in the European Economic Area, the authorized Microsoft employees are located in the European Economic Area. This human review abuse monitoring process will not take place if the customer has been approved for modified abuse monitoring. 
+- **Notification and Action**: When a threshold of abusive behavior has been confirmed based on the preceding steps, the customer is informed of the determination by email. Except in cases of severe or recurring abuse, customers typically have an opportunity to explain or remediate—and implement mechanisms to prevent recurrence of—the abusive behavior. Failure to address the behavior—or recurring or severe abuse—may result in suspension or termination of the customer’s access to Azure OpenAI resources and/or capabilities.
 
 ## Modified abuse monitoring 
 

articles/ai-foundry/openai/concepts/advanced-prompt-engineering.md

@@ -13,13 +13,13 @@ keywords: ChatGPT, GPT-4, meta prompts, chain of thought
 
 # System message design
 
-This guide will walk you through some techniques in system message design. 
+This guide walks you through some techniques in system message design. 
 
 
 
 ## What is a system message?
 
-A system message is a feature-specific set of instructions or contextual frameworks given to a generative AI model (e.g. GPT-4o, GPT-3.5 Turbo, etc.) to direct and improve the quality and safety of a model’s output. This is particularly helpful in situations that need certain degrees of formality, technical language, or industry-specific terms.  
+A system message is a feature-specific set of instructions or contextual frameworks given to a generative AI model (for example GPT-4o, GPT-3.5 Turbo, etc.) to direct and improve the quality and safety of a model’s output. This is particularly helpful in situations that need certain degrees of formality, technical language, or industry-specific terms.  
 
 
 There is no prescribed length. A system message can be one short sentence:  
@@ -34,7 +34,7 @@ A system message can also be *many* lines long, containing detailed rules, detai
 
 The system message is included at the beginning of the prompt and is used to prime the model with context, instructions, or other information relevant to your use case. You can use the system message to describe the assistant’s personality, define what the model should and shouldn’t answer, and define the format of model responses.
 
-The example below, shows a sample system message and the resulting model response:
+The following example shows a sample system message and the resulting model response:
 
 | System message |User     | Assistant  |
 |----------------|---------|------------|