Merge pull request #266523 from mrbullwinkle/mrb_02_16_2024_freshness

[Azure OpenAI] Freshness + acrolinx updates

Author: JamesJBarnett

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

@@ -1,12 +1,12 @@
 ---
 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, GPT-35-Turbo, and GPT-4 models
-author: suhridpalsule
+description: Learn about the options for how to use prompt engineering with GPT-3, GPT-35-Turbo, and GPT-4 models.
+author: mrbullwinkle
 ms.author: mbullwin 
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 04/20/2023
+ms.date: 02/16/2024
 manager: nitinme
 keywords: ChatGPT, GPT-4, prompt engineering, meta prompts, chain of thought
 zone_pivot_groups: openai-prompt
@@ -23,7 +23,7 @@ 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 GPT-35-Turbo and GPT-4 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 GPT-35-Turbo 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.
 
 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=/azure/ai-services/openai/context/context#limitations), is just as important as understanding how to leverage their strengths.
 

articles/ai-services/openai/how-to/switching-endpoints.md

@@ -7,7 +7,7 @@ ms.author: mbullwin
 ms.service: azure-ai-openai
 ms.custom: devx-track-python
 ms.topic: how-to
-ms.date: 01/06/2023
+ms.date: 02/16/2024
 manager: nitinme
 ---