author metadata update + commit to allow original author to show up as contributor

Author: mrbullwinkle

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

@@ -2,7 +2,7 @@
 title: Prompt Engineering Techniques with Azure OpenAI
 titleSuffix: Azure OpenAI Service
 description: Learn about the options for how to use prompt engineering with GPT-3, ChatGPT, and GPT-4 models
-author: marcotcr
+author: slundberg
 ms.author: mbullwin 
 ms.service: cognitive-services
 ms.topic: conceptual 
@@ -21,9 +21,9 @@ While the principles of prompt engineering can be generalized across many differ
 - Chat Completion API.
 - Completion API.
 
-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) stored inside an array of dictionaries.
+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) stored inside 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. Technically the ChatGPT (preview) models can be used with either APIs, but we strongly recommend using the Chat Completion API for these models. To learn more, please consult our [in-depth guide on using these APIs](../how-to/chatgpt.md).
+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 APIs, but we strongly recommend using the Chat Completion API for these models. To learn more, please consult our [in-depth guide on using these APIs](../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 LLMs](/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.