fixing tracking links

00-course-setup/README.md

@@ -36,7 +36,7 @@ While you wait for your application to be processed, each coding lesson also inc
 
 ## Using the Azure OpenAI Service for the First Time
 
-If this is your first time working with the Azure OpenAI service, please follow this guide on how to [create and deploy an Azure OpenAI Service resource.](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal)
+If this is your first time working with the Azure OpenAI service, please follow this guide on how to [create and deploy an Azure OpenAI Service resource.](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal&WT.mc_id=academic-105485-koreyst)
 
 ## Meet Other Learners
 

02-exploring-and-comparing-different-llms/README.md

@@ -99,14 +99,14 @@ Imagine that we can have someone as well who could create and review the quiz, t
 
 Now, let's talk about the difference between a service and a model. A service is a product that is offered by a Cloud Service Provider, and is often a combination of models, data, and other components. A model is the core component of a service, and is often a foundation model, such as an LLM.
 
-Services are often optimized for production use and are often easier to use than models, via a graphical user interface. However, services are not always available for free, and may require a subscription or payment to use, in exchange to leverage service owner’s equipment and resources, optimizing expenses and scaling easily. An example of service is [Azure OpenAI service](https://learn.microsoft.com/azure/ai-services/openai/overview), which offers a pay-as-you-go rate plan,  meaning users are charged proportionally to how much they use the service Also, Azure OpenAI service offers enterprise-grade security and responsible AI framework on top of the models' capabilities.
+Services are often optimized for production use and are often easier to use than models, via a graphical user interface. However, services are not always available for free, and may require a subscription or payment to use, in exchange to leverage service owner’s equipment and resources, optimizing expenses and scaling easily. An example of service is [Azure OpenAI service](https://learn.microsoft.com/azure/ai-services/openai/overview?WT.mc_id=academic-105485-koreyst), which offers a pay-as-you-go rate plan,  meaning users are charged proportionally to how much they use the service Also, Azure OpenAI service offers enterprise-grade security and responsible AI framework on top of the models' capabilities.
 
 Models are just the Neural Network, with the parameters, weights, and others. Allowing companies to run locally, however, would need to buy equipment, build structure to scale and buy a license or use an open-source model. A model like LLaMA is available to be used, requiring computational power to run the model.
 
 ## How to test and iterate with different models to understand performance on Azure
 
 Once our team has explored the current LLMs landscape and identified some good candidates for their scenarios, the next step is testing them on their data and on their workload. This is an iterative process, done by experiments and measures.
-Most of the models we mentioned in previous paragraphs (OpenAI models, open source models like Llama2, and Hugging Face transformers) are available in the [Foundation Models](https://learn.microsoft.com/azure/machine-learning/concept-foundation-models) catalog in [Azure Machine Learning studio](https://ml.azure.com/).
+Most of the models we mentioned in previous paragraphs (OpenAI models, open source models like Llama2, and Hugging Face transformers) are available in the [Foundation Models](https://learn.microsoft.com/azure/machine-learning/concept-foundation-models?WT.mc_id=academic-105485-koreyst) catalog in [Azure Machine Learning studio](https://ml.azure.com/).
 
 [Azure Machine Learning](https://azure.microsoft.com/products/machine-learning/) is a Cloud Service designed for data scientists and ML engineers to manage the whole ML lifecycle (train, test, deploy and handle MLOps) in a single platform. The Machine Learning studio offers a graphical user interface to this service and enables the user to:
 
@@ -157,7 +157,7 @@ Prompt engineering with context is the most cost-effective approach to kick-off
 ### Retrieval Augmented Generation (RAG)
 
 LLMs have the limitation that they can use only the data that has been used during their training to generate an answer. This means that they don’t know anything about the facts that happened after their training process, and they cannot access non-public information (like company data).
-This can be overcome through RAG, a technique that augments prompt with external data in the form of chunks of documents, considering prompt length limits. This is supported by Vector database tools (like [Azure Vector Search](https://learn.microsoft.com/azure/search/vector-search-overview)) that retrieve the useful chunks from varied pre-defined data sources and add them to the prompt Context.
+This can be overcome through RAG, a technique that augments prompt with external data in the form of chunks of documents, considering prompt length limits. This is supported by Vector database tools (like [Azure Vector Search](https://learn.microsoft.com/azure/search/vector-search-overview?WT.mc_id=academic-105485-koreyst)) that retrieve the useful chunks from varied pre-defined data sources and add them to the prompt Context.
 
 This technique is very helpful when a business doesn’t have enough data, enough time, or resources to fine-tune an LLM, but still wishes to improve performance on a specific workload and reduce risks of hallucinations, i.e., mystification of reality or harmful content.  
 
@@ -188,7 +188,7 @@ A:3, if you have the time and resources and high quality data, fine-tuning is th
 
 ## 🚀 Challenge
 
-Read up more on how you can [use RAG](https://learn.microsoft.com/azure/search/retrieval-augmented-generation-overview) for your business.
+Read up more on how you can [use RAG](https://learn.microsoft.com/azure/search/retrieval-augmented-generation-overview?WT.mc_id=academic-105485-koreyst) for your business.
 
 ## Great Work, Continue Your Learning
 

03-using-generative-ai-responsibly/README.MD

@@ -110,7 +110,7 @@ Building an operational practice around your AI applications is the final stage.
 
 ## Tools
 
-While the work of developing Responsible AI solutions may seem like a lot, it is work well worth the effort. As the area of Generative AI grows, more tooling to help developers efficiently integrate responsibility into their workflows will mature. For example, the [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview ) can help detect harmful content and images via an API request.
+While the work of developing Responsible AI solutions may seem like a lot, it is work well worth the effort. As the area of Generative AI grows, more tooling to help developers efficiently integrate responsibility into their workflows will mature. For example, the [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-koreyst ) can help detect harmful content and images via an API request.
 
 ## Knowledge check
 
@@ -124,7 +124,7 @@ A: 2 and 3 is correct. Responsible AI helps you consider how to mitigate harmful
 
 ## 🚀 Challenge
 
-Read up on [Azure AI Content Saftey](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/overview ) and see what you can adopt for your usage.
+Read up on [Azure AI Content Saftey](https://learn.microsoft.com/azure/ai-services/content-safety/overview?WT.mc_id=academic-105485-koreyst) and see what you can adopt for your usage.
 
 ## Great Work, Continue Your Learning
 

04-prompt-engineering-fundamentals/README.md

@@ -186,7 +186,7 @@ Let's start with the basic prompt: a text input sent to the model with no other
 
 ### Complex Prompt
 
-Now let's add context and instructions to that basic prompt. The [Chat Completion API](https://learn.microsoft.com/azure/ai-services/openai/how-to/chatgpt) lets us construct a complex prompt as a collection of _messages_ with:
+Now let's add context and instructions to that basic prompt. The [Chat Completion API](https://learn.microsoft.com/azure/ai-services/openai/how-to/chatgpt?WT.mc_id=academic-105485-koreyst) lets us construct a complex prompt as a collection of _messages_ with:
 
 - Input/output pairs reflecting _user_ input and _assistant_ response.
 - System message setting the context for assistant behavior or personality.
@@ -321,7 +321,7 @@ Prompt Engineering is a trial-and-error process so keep three broad guiding fact
 
 ## Best Practices
 
-Now let's look at common best practices that are recommended by [Open AI](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api) and [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering#best-practices) practitioners.
+Now let's look at common best practices that are recommended by [Open AI](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api) and [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/concepts/prompt-engineering#best-practices?WT.mc_id=academic-105485-koreyst) practitioners.
 
 ```text
 | What | Why |

06-text-generation-apps/README.md

@@ -87,13 +87,13 @@ pip install openai
 You need to carry out the following steps:
 
 - Create an account on Azure <https://azure.microsoft.com/free/>.
-- Gain access to Azure Open AI. Go to <https://learn.microsoft.com/en-us/azure/ai-services/openai/overview#how-do-i-get-access-to-azure-openai> and request access.
+- Gain access to Azure Open AI. Go to <https://learn.microsoft.com/en-us/azure/ai-services/openai/overview#how-do-i-get-access-to-azure-openai?WT.mc_id=academic-105485-koreyst> and request access.
 
   > [!NOTE]
   > At the time of writing, you need to apply for access to Azure Open AI.
 
 - Install Python <https://www.python.org/>
-- Have created an Azure OpenAI Service resource. See this guide for how to [create a resource](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).
+- Have created an Azure OpenAI Service resource. See this guide for how to [create a resource](https://learn.microsoft.com/azure/ai-services/openai/how-to/create-resource?pivots=web-portal?WT.mc_id=academic-105485-koreyst).
 
 ### Locate API key and endpoint
 

07-building-chat-applications/README.md

@@ -96,7 +96,7 @@ This "profile" prompts ChatGPT to create a lesson plan on linked lists. Notice t
 
 ### Microsoft's System Message Framework for Large Language Models
 
-[Microsoft has provided guidance](https://learn.microsoft.com/azure/ai-services/openai/concepts/system-message#define-the-models-output-format) for writing effective system messages when generating responses from LLMs broken down into 4 areas:
+[Microsoft has provided guidance](https://learn.microsoft.com/azure/ai-services/openai/concepts/system-message#define-the-models-output-format?WT.mc_id=academic-105485-koreyst) for writing effective system messages when generating responses from LLMs broken down into 4 areas:
 
 1. Defining who the model is for, as well as its capabilities and limitations.
 2. Defining the model's output format.

07-building-chat-applications/notebook-azure-openai.ipynb

@@ -31,7 +31,7 @@
         "\n",
         "The Python OpenAI API works with Azure OpenAI as well, with a few modifications. Learn more about the differences here: [How to switch between OpenAI and Azure OpenAI endpoints with Python](https://learn.microsoft.com/azure/ai-services/openai/how-to/switching-endpoints?WT_mc_id=academic-109527-jasmineg)\n",
         "\n",
-        "For more quickstart examples please refer to the official Azure Open AI Quickstart Documentation https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?pivots=programming-language-studio"
+        "For more quickstart examples please refer to the official Azure Open AI Quickstart Documentation https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?pivots=programming-language-studio?WT.mc_id=academic-105485-koreyst"
       ]
     },
     {

08-building-search-applications/README.md

@@ -2,7 +2,7 @@
 
 [![Introduction to Generative AI and Large Language Models](./media/genai_course_8[80].png)](TBD)
 
-> **Video Coming Soon** 
+> **Video Coming Soon**
 
 There's more to LLMs than chat bots and text generation. It's also possible to build search applications using Embeddings. Embeddings are numerical representations of data also known as vectors, and can be used for semantic search for data.
 
@@ -66,14 +66,14 @@ The Embedding index for this lesson was created with a series of Python scripts.
 The scripts perform the following operations:
 
 1. The transcript for each YouTube video in the [AI Show](https://www.youtube.com/playlist?list=PLlrxD0HtieHi0mwteKBOfEeOYf0LJU4O1) playlist is downloaded.
-2. Using [OpenAI Functions](https://learn.microsoft.com/azure/ai-services/openai/how-to/function-calling), an attempt is made to extract the speaker name from the first 3 minutes of the YouTube transcript. The speaker name for each video is stored in the Embedding Index named `embedding_index_3m.json`.
+2. Using [OpenAI Functions](https://learn.microsoft.com/azure/ai-services/openai/how-to/function-calling?WT.mc_id=academic-105485-koreyst), an attempt is made to extract the speaker name from the first 3 minutes of the YouTube transcript. The speaker name for each video is stored in the Embedding Index named `embedding_index_3m.json`.
 3. The transcript text is then chunked into **3 minute text segments**. The segment includes about 20 words overlapping from the next segment to ensure that the Embedding for the segment is not cut off and to provide better search context.
 4. Each text segment is then passed to the OpenAI Chat API to summarize the text into 60 words. The summary is also stored in the Embedding Index `embedding_index_3m.json`.
 5. Finally, the segment text is passed to the OpenAI Embedding API. The Embedding API returns a vector of 1536 numbers that represent the semantic meaning of the segment. The segment along with the OpenAI Embedding vector is stored in an Embedding Index `embedding_index_3m.json`.
 
 ### Vector Databases
 
-For lesson simplicity, the Embedding Index is stored in a JSON file named `embedding_index_3m.json` and loaded into a Pandas Dataframe. However, in production, the Embedding Index would be stored in a vector database such as [Azure Cognitive Search](https://learn.microsoft.com/training/modules/improve-search-results-vector-search), [Redis](https://cookbook.openai.com/examples/vector_databases/redis/readme), [Pinecone](https://cookbook.openai.com/examples/vector_databases/pinecone/readme), [Weaviate](https://cookbook.openai.com/examples/vector_databases/weaviate/readme), to name but a few.
+For lesson simplicity, the Embedding Index is stored in a JSON file named `embedding_index_3m.json` and loaded into a Pandas Dataframe. However, in production, the Embedding Index would be stored in a vector database such as [Azure Cognitive Search](https://learn.microsoft.com/training/modules/improve-search-results-vector-search?WT.mc_id=academic-105485-koreyst), [Redis](https://cookbook.openai.com/examples/vector_databases/redis/readme), [Pinecone](https://cookbook.openai.com/examples/vector_databases/pinecone/readme), [Weaviate](https://cookbook.openai.com/examples/vector_databases/weaviate/readme), to name but a few.
 
 ## Understanding cosine similarity
 

08-building-search-applications/scripts/README.md

@@ -8,7 +8,7 @@ The transcription data prep scripts have been tested on the latest releases Wind
 
 > [!IMPORTANT]
 > We suggest you update the Azure CLI to the latest version to ensure compatibility with OpenAI
-> See [Documentation](https://learn.microsoft.com/en-us/cli/azure/update-azure-cli)
+> See [Documentation](https://learn.microsoft.com/en-us/cli/azure/update-azure-cli?WT.mc_id=academic-105485-koreyst)
 
 1. Create a resource group
 

10-building-low-code-ai-applications/README.md

@@ -174,7 +174,7 @@ Some of the Prebuilt AI Models available in Power Platform include:
 - **Form Processing**: This model extracts information from forms.
 - **Invoice Processing**: This model extracts information from invoices.
 
-With Custom AI Models you can bring your own model into AI Builder so that it can function like any AI Builder custom model, allowing you to train the model using your own data. You can use these models to automate processes and predict outcomes in both Power Apps and Power Automate. When using your own model there are limitations that apply. Read more on these [limitations](https://learn.microsoft.com/ai-builder/byo-model#limitations).
+With Custom AI Models you can bring your own model into AI Builder so that it can function like any AI Builder custom model, allowing you to train the model using your own data. You can use these models to automate processes and predict outcomes in both Power Apps and Power Automate. When using your own model there are limitations that apply. Read more on these [limitations](https://learn.microsoft.com/ai-builder/byo-model#limitations?WT.mc_id=academic-105485-koreyst).
 
 ![AI builder models](images/ai-builder-models.png)
 

11-integrating-with-function-calling/Lesson11-FunctionCalling.ipynb

@@ -558,7 +558,7 @@
    "source": [
     "## Code Challenge \n",
     "\n",
-    "Great work! To continue your learning of Azure Open AI Function Calling you can build: https://learn.microsoft.com/en-us/training/support/catalog-api-developer-reference \n",
+    "Great work! To continue your learning of Azure Open AI Function Calling you can build: https://learn.microsoft.com/en-us/training/support/catalog-api-developer-reference?WT.mc_id=academic-105485-koreyst \n",
     " - More parameters of the function that might help learners find more courses. You can find the available API parameters here: \n",
     " - Create another function call that takes more information from the learner like their native language \n",
     " - Create error handling on when the function call and/or API call does not return any suitable courses "

11-integrating-with-function-calling/README.md

@@ -442,7 +442,7 @@ To continue your learning of Azure Open AI Function Calling you can build:
 - Create another function call that takes more information from the learner like their native language
 - Create error handling on when the function call and/or API call does not return any suitable courses
 
- Hint: Follow the [Learn API reference documentation](https://learn.microsoft.com/training/support/catalog-api-developer-reference) page to see how and where this data is available.
+ Hint: Follow the [Learn API reference documentation](https://learn.microsoft.com/training/support/catalog-api-developer-reference?WT.mc_id=academic-105485-koreyst) page to see how and where this data is available.
 
 ## Great Work! Continue the Journey
 

12-designing-ux-for-ai-applications/README.md

@@ -26,7 +26,7 @@ After taking this lesson, you'll be able to:
 
 ### Prerequisite
 
-Take some time and read more about [user experience and design thinking.](https://learn.microsoft.com/training/modules/ux-design/)
+Take some time and read more about [user experience and design thinking.](https://learn.microsoft.com/training/modules/ux-design?WT.mc_id=academic-105485-koreyst)
 
 ## Introduction to User Experience and Understanding User Needs