add ()

Author: sdgilley

articles/machine-learning/concept-model-catalog.md

@@ -67,7 +67,7 @@ Other models | Available | Not available
 
 ## Managed compute
 
-The capability to deploy models with managed compute builds on platform capabilities of Azure Machine Learning to enable seamless integration, across the entire GenAIOps lifecycle, of the wide collection of models in the model catalog. 
+The capability to deploy models with managed compute builds on platform capabilities of Azure Machine Learning to enable seamless integration, across the entire GenAIOps (sometimes called LLMOps) lifecycle, of the wide collection of models in the model catalog. 
 
 :::image type="content" source="media/concept-model-catalog/llm-ops-life-cycle.png" alt-text="A diagram showing the LLMops life cycle." lightbox="media/concept-model-catalog/llm-ops-life-cycle.png":::
 

articles/machine-learning/prompt-flow/concept-llmops-maturity.md

@@ -1,7 +1,7 @@
 ---
 title: Advance your maturity level for GenAIOps
 titleSuffix: Azure Machine Learning
-description: Learn about the different stages of Large Language Operations (GenAIOps) and how to advance your organization's capabilities.
+description: Learn about the different stages of Generative Artificial Intelligence Operations (GenAIOps) and how to advance your organization's capabilities.
 services: machine-learning
 ms.service: azure-machine-learning
 ms.subservice: prompt-flow
@@ -12,9 +12,9 @@ ms.reviewer: sasahami
 ms.date: 03/28/2024
 ---
 
-# Advance your maturity level for Large Language Model Operations (GenAIOps)
+# Advance your maturity level for Generative Artificial Intelligence Operations (GenAIOps)
 
-Large Language Model Operations, or **GenAIOps**, describes the operational practices and strategies for managing large language models (LLMs) in production. This article  provides guidance on how to advance your capabilities in GenAIOps, based on your organization's current maturity level.
+Generative Artificial Intelligence Operations, or **GenAIOps** (sometimes called LLMOps), describes the operational practices and strategies for managing large language models (LLMs) in production. This article  provides guidance on how to advance your capabilities in GenAIOps, based on your organization's current maturity level.
 
 :::image type="content" source="../media/concept-llmops-maturity/llmopsml.png" alt-text="Diagram shows maturity level of GenAIOps." lightbox="../media/concept-llmops-maturity/llmopsml.png":::
 

articles/machine-learning/prompt-flow/how-to-end-to-end-azure-devops-with-prompt-flow.md

@@ -19,7 +19,7 @@ ms.custom:
 
 # GenAIOps with prompt flow and Azure DevOps
 
-Large Language Operations, or **GenAIOps**, has become the cornerstone of efficient prompt engineering and LLM-infused application development and deployment. As the demand for LLM-infused applications continues to soar, organizations find themselves in need of a cohesive and streamlined process to manage their end-to-end lifecycle.
+Generative Artificial Intelligence Operations, or **GenAIOps** (sometimes called LLMOps), has become the cornerstone of efficient prompt engineering and LLM-infused application development and deployment. As the demand for LLM-infused applications continues to soar, organizations find themselves in need of a cohesive and streamlined process to manage their end-to-end lifecycle.
 
 Azure Machine Learning allows you to integrate with **Azure DevOps** to automate the LLM-infused application development lifecycle with prompt flow.
 

articles/machine-learning/prompt-flow/how-to-end-to-end-llmops-with-prompt-flow.md

@@ -17,7 +17,7 @@ ms.date: 01/02/2024
 
 # GenAIOps with prompt flow and GitHub
 
-Large Language Models Operations, or **GenAIOps**, has become the cornerstone of efficient prompt engineering and LLM-infused application development and deployment. As the demand for LLM-infused applications continues to soar, organizations find themselves in need of a cohesive and streamlined process to manage their end-to-end lifecycle.
+Generative Artificial Intelligence Operations, or **GenAIOps** (sometimes called LLMOps), has become the cornerstone of efficient prompt engineering and LLM-infused application development and deployment. As the demand for LLM-infused applications continues to soar, organizations find themselves in need of a cohesive and streamlined process to manage their end-to-end lifecycle.
 
 Azure Machine Learning allows you to integrate with GitHub to automate the LLM-infused application development lifecycle with prompt flow.
 

articles/machine-learning/prompt-flow/how-to-integrate-with-llm-app-devops.md

@@ -29,7 +29,7 @@ This documentation focuses on how to effectively combine the capabilities of pro
 
 When developing applications using LLM, it's common to have a standardized application engineering process that includes code repositories and CI/CD pipelines. This integration allows for a streamlined development process, version control, and collaboration among team members.
 
-For developers experienced in code development who seek a more efficient GenAIOps iteration process, the following key features and benefits you can gain from prompt flow code experience:
+For developers experienced in code development who seek a more efficient GenAIOps (sometimes called LLMOps) iteration process, the following key features and benefits you can gain from prompt flow code experience:
 
 - **Flow versioning in code repository**. You can define your flow in YAML format, which can stay aligned with the referenced source files in a folder structure. 
 - **Integrate flow run with CI/CD pipeline**. You can trigger flow runs using the prompt flow CLI or SDK, which can be seamlessly integrated into your CI/CD pipeline and delivery process.