Updated links

01-introduction-to-genai/README.md

@@ -112,7 +112,7 @@ Also, the output of a generative AI model is not perfect and sometimes the creat
 
 Your assignment is to read up more on [generative AI](https://en.wikipedia.org/wiki/Generative_artificial_intelligence) and try to identify an area where you would add generative AI today that doesn't have it. How would the impact be different from doing it the "old way", can you do something you couldn't before, or are you faster? Write a 300 word summary on what your dream AI startup would look like and include headers like "Problem", "How I would use AI", "Impact" and optionally a business plan. 
 
-If you done this task, you might even be ready to apply to Microsoft's incubator, [Microsoft for Startups Founders Hub](https://www.microsoft.com/en-gb/startups) we offer credits for both Azure, OpenAI, mentoring and much more, check it out!
+If you done this task, you might even be ready to apply to Microsoft's incubator, [Microsoft for Startups Founders Hub](https://www.microsoft.com/en-gb/startups?WT.mc_id=academic-105485-koreyst) we offer credits for both Azure, OpenAI, mentoring and much more, check it out!
 
 ## Knowledge check
 
@@ -126,7 +126,7 @@ A: 3, an LLM is non-deterministic, the response vary, however, you can control i
 
 ## Great Work! Continue the Journey 
 
-Want to learn more about different Generative AI concepts? Go to the [continued learning page](../13-continued-learning/README.md) to find other great resources on this topic. 
+Want to learn more about different Generative AI concepts? Go to the [continued learning page](../13-continued-learning/README.md?WT.mc_id=academic-105485-koreyst) to find other great resources on this topic. 
 
-Head over to the Lesson 2 where we will look at how to [explore and compare different LLM types](/02-exploring-and-comparing-different-llms/README.md)! 
+Head over to the Lesson 2 where we will look at how to [explore and compare different LLM types](/02-exploring-and-comparing-different-llms/README.md?WT.mc_id=academic-105485-koreyst)! 
 

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

@@ -108,7 +108,7 @@ Models are just the Neural Network, with the parameters, weights, and others. Al
 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?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:
+[Azure Machine Learning](https://azure.microsoft.com/products/machine-learning/?WT.mc_id=academic-105485-koreyst) 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:
 
 - Find the Foundation Model of interest in the catalog, filtering by task, license, or name. It’s also possible to import new models that are not yet included in the catalog.
 - Review the model card, including a detailed description and code samples, and test it with the Sample Inference widget, by providing a sample prompt to test the result.
@@ -192,6 +192,6 @@ Read up more on how you can [use RAG](https://learn.microsoft.com/azure/search/r
 
 ## Great Work, Continue Your Learning
 
-Want to learn more about different Generative AI concepts? Go to the [continued learning page](../13-continued-learning/README.md) to find other great resources on this topic.
+Want to learn more about different Generative AI concepts? Go to the [continued learning page](../13-continued-learning/README.md?WT.mc_id=academic-105485-koreyst) to find other great resources on this topic.
 
-Head over to the Lesson 3 where we will look at how to [build with Generative AI Responsibly](/03-using-generative-ai-responsibly/README.MD)!
+Head over to the Lesson 3 where we will look at how to [build with Generative AI Responsibly](/03-using-generative-ai-responsibly/README.MD?WT.mc_id=academic-105485-koreyst)!

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

@@ -96,11 +96,11 @@ It is now time to find ways where we can prevent or limit the potential harm cau
 
 - **Metaprompt**. Metaprompts and grounding are ways we can direct or limit the model based on certain behaviors and information. This could be using system inputs to define certain limits of the model. In addition, providing outputs that are more relevant to scope or domain of system.
 
- It can also be using techniques like Retrieval Augmented Generation (RAG) to have the model only pull information from a selection of trusted sources. There is a lesson later in this course for [building search applications](../08-building-search-applications/README.md)
+ It can also be using techniques like Retrieval Augmented Generation (RAG) to have the model only pull information from a selection of trusted sources. There is a lesson later in this course for [building search applications](../08-building-search-applications/README.md?WT.mc_id=academic-105485-koreyst)
 
 - **User Experience**. The final layer is where the user interacts directly with the model through our application’s interface in some way. In this way we can design the UI/UX to limit the user on the types of inputs they can send to the model as well as text or images displayed to the user. When deploying the AI application, we also must be transparent about what our Generative AI application can and can’t do.  
 
-We have an entire lesson dedicated to [Designing UX for AI Applications](../12-designing-ux-for-ai-applications/README.md)
+We have an entire lesson dedicated to [Designing UX for AI Applications](../12-designing-ux-for-ai-applications/README.md?WT.mc_id=academic-105485-koreyst)
 
 - **Evaluate model**. Working with LLMs can be challenging because we don’t always have control over the data the model was trained on. Regardless, we should always evaluate the model’s performance and outputs. It’s still important to measure the model’s accuracy, similarity, groundedness, and relevance of the output. This helps provide transparency and trust to stakeholders and users.
 
@@ -128,6 +128,6 @@ Read up on [Azure AI Content Saftey](https://learn.microsoft.com/azure/ai-servic
 
 ## Great Work, Continue Your Learning
 
-Want to learn more about how to build with Generative AI responsibly? Go to the [continued learning page](../13-continued-learning/README.md) to find other great resources on this topic.
+Want to learn more about how to build with Generative AI responsibly? Go to the [continued learning page](../13-continued-learning/README.md?WT.mc_id=academic-105485-koreyst) to find other great resources on this topic.
 
-Head over to Lesson 4 where we will look at [Prompt Engineering Fundamentals](/4-prompt-engineering-fundamentals/README.md)!
+Head over to Lesson 4 where we will look at [Prompt Engineering Fundamentals](/4-prompt-engineering-fundamentals/README.md?WT.mc_id=academic-105485-koreyst)!

04-prompt-engineering-fundamentals/README.md

@@ -40,13 +40,13 @@ The notebook comes with _starter_ exercises - but you are encouraged to add your
 
 ## Our Startup
 
-Now, let's talk about how _this topic_ relates to our startup mission to [bring AI innovation to education](https://educationblog.microsoft.com/2023/06/collaborating-to-bring-ai-innovation-to-education). We want to build AI-powered applications of  _personalized learning_ - so let's think about how different users of our application might "design" prompts:
+Now, let's talk about how _this topic_ relates to our startup mission to [bring AI innovation to education](https://educationblog.microsoft.com/2023/06/collaborating-to-bring-ai-innovation-to-education?WT.mc_id=academic-105485-koreyst). We want to build AI-powered applications of  _personalized learning_ - so let's think about how different users of our application might "design" prompts:
 
 - **Administrators** might ask the AI to _analyze curriculum data to identify gaps in coverage_. The AI can summarize results or visualize them with code.
 - **Educators** might ask the AI to _generate a lesson plan for a target audience and topic_. The AI can build the personalized plan in a specified format.
 - **Students** might ask the AI to _tutor them in a difficult subject_. The AI can now guide students with lessons, hints & examples tailored to their level.
 
-That's just the tip of the iceberg. Check out [Prompts For Education](https://github.com/microsoft/prompts-for-edu/tree/main) - an open-source prompts library curated by education experts - to get a broader sense of the possibilities! _Try running some of those prompts in the sandbox or using the OpenAI Playground to see what happens!_
+That's just the tip of the iceberg. Check out [Prompts For Education](https://github.com/microsoft/prompts-for-edu/tree/main?WT.mc_id=academic-105485-koreyst) - an open-source prompts library curated by education experts - to get a broader sense of the possibilities! _Try running some of those prompts in the sandbox or using the OpenAI Playground to see what happens!_
 
 <!--
 LESSON TEMPLATE:
@@ -145,20 +145,20 @@ Prompt engineering techniques like _metaprompting_ and _temperature configuratio
 
 ## Case Study: GitHub Copilot
 
-Let's wrap this section by getting a sense for how prompt engineering is used in real-world solutions by looking at one Case Study: [GitHub Copilot](https://github.com/features/copilot).
+Let's wrap this section by getting a sense for how prompt engineering is used in real-world solutions by looking at one Case Study: [GitHub Copilot](https://github.com/features/copilot?WT.mc_id=academic-105485-koreyst).
 
-GitHub Copilot is your "AI Pair Programmer" - it converts text prompts into code completions and is integrated into your development environment (e.g., Visual Studio Code) for a seamless user experience. As documented in the series of blogs below, the earliest version was based on the OpenAI Codex model - with engineers quickly realizing the need to fine-tune the model and develop better prompt engineering techniques, to improve code quality. In July, they [debuted an improved AI model that goes beyond Codex](https://github.blog/2023-07-28-smarter-more-efficient-coding-github-copilot-goes-beyond-codex-with-improved-ai-model/) for even faster suggestions.
+GitHub Copilot is your "AI Pair Programmer" - it converts text prompts into code completions and is integrated into your development environment (e.g., Visual Studio Code) for a seamless user experience. As documented in the series of blogs below, the earliest version was based on the OpenAI Codex model - with engineers quickly realizing the need to fine-tune the model and develop better prompt engineering techniques, to improve code quality. In July, they [debuted an improved AI model that goes beyond Codex](https://github.blog/2023-07-28-smarter-more-efficient-coding-github-copilot-goes-beyond-codex-with-improved-ai-model/?WT.mc_id=academic-105485-koreyst) for even faster suggestions.
 
 Read the posts in order, to follow their learning journey.
 
-- **May 2023** | [GitHub Copilot is Getting Better at Understanding Your Code](https://github.blog/2023-05-17-how-github-copilot-is-getting-better-at-understanding-your-code/)
-- **May 2023** | [Inside GitHub: Working with the LLMs behind GitHub Copilot](https://github.blog/2023-05-17-inside-github-working-with-the-llms-behind-github-copilot/).
-- **Jun 2023** | [How to write better prompts for GitHub Copilot](https://github.blog/2023-06-20-how-to-write-better-prompts-for-github-copilot/).
-- **Jul 2023** | [.. GitHub Copilot goes beyond Codex with improved AI model](https://github.blog/2023-07-28-smarter-more-efficient-coding-github-copilot-goes-beyond-codex-with-improved-ai-model/)
-- **Jul 2023** | [A Developer's Guide to Prompt Engineering and LLMs](https://github.blog/2023-07-17-prompt-engineering-guide-generative-ai-llms/)
-- **Sep 2023** | [How to build an enterprise LLM app: Lessons from GitHub Copilot](https://github.blog/2023-09-06-how-to-build-an-enterprise-llm-application-lessons-from-github-copilot/)
+- **May 2023** | [GitHub Copilot is Getting Better at Understanding Your Code](https://github.blog/2023-05-17-how-github-copilot-is-getting-better-at-understanding-your-code/?WT.mc_id=academic-105485-koreyst)
+- **May 2023** | [Inside GitHub: Working with the LLMs behind GitHub Copilot](https://github.blog/2023-05-17-inside-github-working-with-the-llms-behind-github-copilot/?WT.mc_id=academic-105485-koreyst).
+- **Jun 2023** | [How to write better prompts for GitHub Copilot](https://github.blog/2023-06-20-how-to-write-better-prompts-for-github-copilot/?WT.mc_id=academic-105485-koreyst).
+- **Jul 2023** | [.. GitHub Copilot goes beyond Codex with improved AI model](https://github.blog/2023-07-28-smarter-more-efficient-coding-github-copilot-goes-beyond-codex-with-improved-ai-model/?WT.mc_id=academic-105485-koreyst)
+- **Jul 2023** | [A Developer's Guide to Prompt Engineering and LLMs](https://github.blog/2023-07-17-prompt-engineering-guide-generative-ai-llms/?WT.mc_id=academic-105485-koreyst)
+- **Sep 2023** | [How to build an enterprise LLM app: Lessons from GitHub Copilot](https://github.blog/2023-09-06-how-to-build-an-enterprise-llm-application-lessons-from-github-copilot/?WT.mc_id=academic-105485-koreyst)
 
-You can also browse their [Engineering blog](https://github.blog/category/engineering/) for more posts like [this one](https://github.blog/2023-09-27-how-i-used-github-copilot-chat-to-build-a-reactjs-gallery-prototype/) that shows how these models and techniques are _applied_ for driving real-world applications.
+You can also browse their [Engineering blog](https://github.blog/category/engineering/?WT.mc_id=academic-105485-koreyst) for more posts like [this one](https://github.blog/2023-09-27-how-i-used-github-copilot-chat-to-build-a-reactjs-gallery-prototype/?WT.mc_id=academic-105485-koreyst) that shows how these models and techniques are _applied_ for driving real-world applications.
 
 ---
 
@@ -278,7 +278,7 @@ A prompt template is a _pre-defined recipe for a prompt_ that can be stored and
 
 In it's more complex form like [this example from LangChain](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/) it contains _placeholders_ that can be replaced with data from a variety of sources (user input, system context, external data sources etc.) to generate a prompt dynamically. This allows us to create a library of reusable prompts that can be used to drive consistent user experiences **programmatically** at scale.
 
-Finally, the real value of templates lies in the ability to create and publish _prompt libraries_ for vertical application domains - where the prompt template is now _optimized_ to reflect application-specific context or examples that make the responses more relevant and accurate for the targeted user audience. The [Prompts For Edu](https://github.com/microsoft/prompts-for-edu) repository is a great example of this approach, curating a library of prompts for the education domain with emphasis on key objectives like lesson planning, curriculum design, student tutoring etc.
+Finally, the real value of templates lies in the ability to create and publish _prompt libraries_ for vertical application domains - where the prompt template is now _optimized_ to reflect application-specific context or examples that make the responses more relevant and accurate for the targeted user audience. The [Prompts For Edu](https://github.com/microsoft/prompts-for-edu?WT.mc_id=academic-105485-koreyst) repository is a great example of this approach, curating a library of prompts for the education domain with emphasis on key objectives like lesson planning, curriculum design, student tutoring etc.
 
 ## Supporting Content
 
@@ -396,6 +396,6 @@ See if you can leverage the "cue" technique with the prompt: Complete the senten
 
 ## Great Work! Continue Your Learning
 
-Want to learn more about different Prompt Engineering concepts? Go to the [contiuned learning page](../13-continued-learning/README.md) to find other great resources on this topic.
+Want to learn more about different Prompt Engineering concepts? Go to the [contiuned learning page](../13-continued-learning/README.md?WT.mc_id=academic-105485-koreyst) to find other great resources on this topic.
 
-Head over to the Lesson 5 where we will look at [advance prompting techniques](../05-advanced-prompts/README.md)!
+Head over to the Lesson 5 where we will look at [advance prompting techniques](../05-advanced-prompts/README.md?WT.mc_id=academic-105485-koreyst)!

05-advanced-prompts/README.md

@@ -619,6 +619,6 @@ You just used self-refine technique in the assignment. Take any program you buil
 
 ## Great Work! Continue Your Learning
 
-Want to learn more about creating advanced prompts? Go to the [continued learning page](../13-continued-learning/README.md) to find other great resources on this topic.
+Want to learn more about creating advanced prompts? Go to the [continued learning page](../13-continued-learning/README.md?WT.mc_id=academic-105485-koreyst) to find other great resources on this topic.
 
-Head over to the Lesson 6 where we will apply our knowledge of Prompt Engineering by [building text generation apps](/6-text-generation-apps/README.md)
+Head over to the Lesson 6 where we will apply our knowledge of Prompt Engineering by [building text generation apps](/6-text-generation-apps/README.md?WT.mc_id=academic-105485-koreyst)