Address reviewer feedback

Author: Orin-Thomas

learn-pr/advocates/intro-ai-center-excellence/2-how-coe-assists-planning-adoption-generative-ai.yml renamed to learn-pr/advocates/intro-ai-center-excellence/2-how-center-excellence-assists-planning-adoption-generative-ai.yml

@@ -1,5 +1,5 @@
 ### YamlMime:ModuleUnit
-uid: learn.introduction-generative-ai-center-excellence.how-coe-assists-planning-adoption-generative-ai
+uid: learn.introduction-generative-ai-center-excellence.how-center-excellence-assists-planning-adoption-generative-ai
 title: How the Generative AI CoE assists in planning adoption of generative AI
 metadata:
   title: How the Generative AI CoE assists in planning adoption of generative AI
@@ -10,4 +10,4 @@ metadata:
   ms.topic: unit
 durationInMinutes: 5
 content: |
-  [!include[](includes/2-how-coe-assists-planning-adoption-generative-ai.md)]
+  [!include[](includes/2-how-center-excellence-assists-planning-adoption-generative-ai.md)]

learn-pr/advocates/intro-ai-center-excellence/4-determining-organizational-roles-responsibilities.yml

@@ -3,7 +3,7 @@ uid: learn.introduction-generative-ai-center-excellence.determining-organization
 title: Determining organizational roles and responsibilities
 metadata:
   title: Determining organizational roles and responsibilities
-  description: TBD
+  description: Understand the roles and responsibilities of a Generative AI Center of Excellence.
   ms.date: 04/05/2025
   author: Orin-Thomas
   ms.author: orthomas

learn-pr/advocates/intro-ai-center-excellence/includes/2-how-coe-assists-planning-adoption-generative-ai.md renamed to learn-pr/advocates/intro-ai-center-excellence/includes/2-how-center-excellence-assists-planning-adoption-generative-ai.md

@@ -15,7 +15,7 @@ When implemented correctly, an AI center of excellence ensures the right people
 - Ensuring key business and technical decision-makers, and other stakeholders, are actively involved in generative AI initiatives. 
 - Bridging the gap between technical and leadership to translate technical capabilities into business outcomes.
 
-![Diagram showing the process through which technical and domain experts can collaborate in generative AI lifecycle](../media/technical-domain-expert-collaboration.png)
+![Diagram showing the process through which technical and domain experts can collaborate in generative AI lifecycle.](../media/technical-domain-expert-collaboration.png)
 
 A generative AI CoE is able to identify use cases and ensure the "organizational fit." Domain experts (from business or specific functions) play a crucial role in identifying relevant use cases, determining the necessary data, and evaluating the model's effectiveness, especially considering the unique challenges of generative AI like delusions or model variability. The CoE is able to ensure that use cases must align with the organization's overarching strategy and its ability to deliver on that strategy.
 

learn-pr/advocates/intro-ai-center-excellence/includes/4-determining-organizational-roles-responsibilities.md

@@ -14,8 +14,8 @@ Given generative AI's significance, impact, and potential, it requires dedicated
 
 Foundational AI roles like Data Scientists, ML Engineers, AI Architects, and NLP Engineers are still essential. However, the rise of generative AI necessitates an evolution in their skills and responsibilities. Employees with the following technical specializations are also likely to be needed to meet the unique challenges posed by large language models and generative systems when deployed to meet business objectives.
 
-    - Prompt Engineer. A Prompt Engineer designs and optimizes inputs for generative AI models to guide their behavior and produce accurate, desired outputs. Combining creativity and technical skill, they leverage in-context learning to enable models to perform tasks without extra training. Their work involves crafting clear instructions, experimenting with prompts, and iterating based on results, incorporating elements like role definitions, system instructions, and examples. The goal is to enhance the performance and reliability of large language models (LLMs) through effective prompt design.
-    - AI Agent Engineers. An AI Agent Engineer develops and optimizes autonomous AI systems that can perceive, reason, and act to achieve specific goals. They design architectures, integrate machine learning models, and program decision-making processes to enable agents to interact with environments, adapt to new tasks, and perform complex functions. 
-    - Generative AI Security engineer. A security practitioner who is able to apply appropriate security controls to harden generative AI applications from common attacks. Generative AI security engineers are also able to control the access of generative AI applications to organizational resources so that those applications only have access to appropriate data.
+- Prompt Engineer. A Prompt Engineer designs and optimizes inputs for generative AI models to guide their behavior and produce accurate, desired outputs. Combining creativity and technical skill, they leverage in-context learning to enable models to perform tasks without extra training. Their work involves crafting clear instructions, experimenting with prompts, and iterating based on results, incorporating elements like role definitions, system instructions, and examples. The goal is to enhance the performance and reliability of large language models (LLMs) through effective prompt design.
+- AI Agent Engineers. An AI Agent Engineer develops and optimizes autonomous AI systems that can perceive, reason, and act to achieve specific goals. They design architectures, integrate machine learning models, and program decision-making processes to enable agents to interact with environments, adapt to new tasks, and perform complex functions. 
+- Generative AI Security engineer. A security practitioner who is able to apply appropriate security controls to harden generative AI applications from common attacks. Generative AI security engineers are also able to control the access of generative AI applications to organizational resources so that those applications only have access to appropriate data.
 
 Generative AI Operations. IT operations professionals who understand how to deploy, manage, monitor, backup, and recover AI workloads. Generative AI Operations is responsible for managing the performance of AI workloads, balancing the performance of the workloads against the cost of running those workloads. Small and medium enterprises may not initially have the capability to create and hire for new organizational roles dedicated to AI technologies. Instead, they should ensure that generative AI responsibilities are clearly understood and assigned to existing leadership or key employees. Successful generative AI deployment requires a team or a virtual team who can dedicate time to upskilling, and align AI initiatives with business goals.

learn-pr/advocates/intro-ai-center-excellence/index.yml

@@ -33,7 +33,7 @@ products:
 - azure
 units:
 - learn.introduction-generative-ai-center-excellence.introduction-generative-ai-center-excellence
-- learn.introduction-generative-ai-center-excellence.how-coe-assists-planning-adoption-generative-ai
+- learn.introduction-generative-ai-center-excellence.how-center-excellence-assists-planning-adoption-generative-ai
 - learn.introduction-generative-ai-center-excellence.oversight-generative-ai-deployment-operations-governance
 - learn.introduction-generative-ai-center-excellence.determining-organizational-roles-responsibilities
 - learn.introduction-generative-ai-center-excellence.skilling-your-organization