update

Author: mrbullwinkle

articles/cognitive-services/openai/concepts/advanced-prompt-engineering.md

@@ -23,10 +23,9 @@ While the principles of prompt engineering can be generalized across many differ
 
 Each API requires input data to be formatted differently, which in turn impacts overall prompt design. The Chat Completion API supports the ChatGPT (preview) and GPT-4 (preview) models. These models are designed to take input formatted in a [specific chat-like transcript](../how-to/chatgpt.md) divided across an array of dictionaries.
 
-The Completion API supports the older GPT-3 models and has much more flexible input requirements in that it takes a string of text with no specific format rules.
+The Completion API supports the older GPT-3 models and has much more flexible input requirements in that it takes a string of text with no specific format rules. Technically the ChatGPT (preview) models can be used with either API's, but we strongly recommend using the Chat Completion API for these models. To learn more, please consult our [in-depth guide on using the two API](../how-to/chatgpt.md).
 
-> [!NOTE]
-> Technically the ChatGPT (preview) models can be used with either API's, but we strongly recommend using the Chat Completion API for these models. To learn more, please consult our [in-depth guide on using the two API](../how-to/chatgpt.md). 
+The techniques in this guide will teach you strategies for increasing the accuracy and grounding of responses you generate with a Large Language Model (LLM). It is, however, important to remember that even when using prompt engineering effectively you still need to validate the responses the models generate. Just because a carefully crafted prompt worked well for a particular scenario doesn't necessarily mean it will generalize more broadly to certain use cases. Understanding the [limitations of LLM's](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext#limitations), is just as important as understanding how to leverage their strengths.
 
 ::: zone pivot="programming-language-chat-completions"
 

articles/cognitive-services/openai/includes/prompt-chat-completion.md

@@ -14,6 +14,9 @@ keywords: ChatGPT
 
 This guide does not go in-depth into the mechanics behind the message structure for Chat Completions. If you aren't familiar with interacting with the ChatGPT and GPT-4 models programmatically we recommend reading our [how-to guide on the Chat Completion API first](../how-to/chatgpt.md).  
 
+> [!NOTE]
+> All of the examples in this section of the guide were tested against a base GPT-4 model in English. If you are reading a localized version of this article in another language, these responses represent a localized translation of the English results. To learn more about the potential limitations depending on what language you are using to prompt a model, please consult our [Responsible AI transparency note](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext#limitations).  
+
 ## System message
 
 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 the 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.

articles/cognitive-services/openai/includes/prompt-completion.md

@@ -11,6 +11,9 @@ manager: nitinme
 keywords: ChatGPT
 ---
 
+> [!NOTE]
+> All of the examples in this section of the guide were tested against the `text-davinci-003` model in English. If you are reading a localized version of this article in another language, these responses represent a localized translation of the English results. To learn more about the potential limitations depending on what language you are using to prompt a model, please consult our [Responsible AI transparency note](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fcognitive-services%2Fopenai%2Fcontext%2Fcontext#limitations).  
+
 ## Start with clear instructions
 
 The sequence in which information is fed in the prompt, matters (GPT style models are built in a certain way and that’s the reason behind this). Our research suggests that telling the model the task you want it to do at the beginning of the prompt, before sharing additional contextual information or examples, can help produce higher-quality outputs.