Merge pull request #40 from microsoft/microsoft-ignite-update

Microsoft ignite update

README.md

@@ -10,13 +10,13 @@ This repository is for the AI Tour workshop: Interacting with Large Language Mod
 
 ## Session Description
 
-This workshop is designed to give you a hands-on introduction to the core concepts and best practices for interacting with OpenAI models in Azure AI Studio. If you have been provided with a Skillable Environment, you'll be using the VM and pre-provisioned Azure resources provided to you to complete the lab. If you are running this workshop on your own, you will need to have an Azure subscription and provision the resources yourself by deploying the resources to Azure.
+This workshop is designed to give you a hands-on introduction to the core concepts and best practices for interacting with OpenAI models in Azure AI Foundry portal. If you have been provided with a Skillable Environment, you'll be using the VM and pre-provisioned Azure resources provided to you to complete the lab. If you are running this workshop on your own, you will need to have an Azure subscription and provision the resources yourself by deploying the resources to Azure.
 
 [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fmicrosoft%2Faitour-interact-with-llms%2Fmain%2Flab%2FWorkshop%20Instructions%2Fassets%2FAITour24_WKR540_Template.json) 
 
 ### Abstract
 
-This lab provides a hands-on and engaging learning opportunity for working with Large Language Models. Learn how to use methods such as few-shot learning and chain of thought. See the creative possibilities of generative AI for image creation and multi-modal scenarios, master the skill of function calling and understand how the model can apply existing knowledge.
+Innovate with Azure OpenAI's GPT-4o multimodal model in this hands-on experience. Learn the core concepts to and best practices to effectively generate with text, sound and images. Experience creating AI assistants with function calling that enhance user experiences and drive innovation.
 
 ### Duration
 75 Minutes
@@ -31,7 +31,7 @@ This lab provides a hands-on and engaging learning opportunity for working with
 * Implementing function calling in LLM applications​
 
 ## Technology Used
-* Azure AI Studio
+* Azure AI Foundry portal
 
 ## Workshop Instructions
 
@@ -82,8 +82,8 @@ Microsoft’s approach to responsible AI is grounded in our AI principles of f
 
 Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the [Azure OpenAI service Transparency note](https://learn.microsoft.com/legal/cognitive-services/openai/transparency-note?tabs=text) to be informed about risks and limitations.
 
-The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview) provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. Within Azure AI Studio, the Content Safety service allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following [quickstart documentation](https://learn.microsoft.com/azure/ai-services/content-safety/quickstart-text?tabs=visual-studio%2Clinux&pivots=programming-language-rest) guides you through making requests to the service.
+The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. [Azure AI Content Safety](https://learn.microsoft.com/azure/ai-services/content-safety/overview) provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. Within Azure AI Foundry portal, the Content Safety service allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following [quickstart documentation](https://learn.microsoft.com/azure/ai-services/content-safety/quickstart-text?tabs=visual-studio%2Clinux&pivots=programming-language-rest) guides you through making requests to the service.
 
 Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using [Performance and Quality and Risk and Safety evaluators](https://learn.microsoft.com/azure/ai-studio/concepts/evaluation-metrics-built-in). You also have the ability to create and evaluate with [custom evaluators](https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#custom-evaluators).
 
-You can evaluate your AI application in your development environment using the [Azure AI Evaluation SDK](https://microsoft.github.io/promptflow/index.html). Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the azure ai evaluation sdk to evaluate your system, you can follow the [quickstart guide](https://learn.microsoft.com/azure/ai-studio/how-to/develop/flow-evaluate-sdk). Once you execute an evaluation run, you can [visualize the results in Azure AI Studio](https://learn.microsoft.com/azure/ai-studio/how-to/evaluate-flow-results).
+You can evaluate your AI application in your development environment using the [Azure AI Evaluation SDK](https://microsoft.github.io/promptflow/index.html). Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the azure ai evaluation sdk to evaluate your system, you can follow the [quickstart guide](https://learn.microsoft.com/azure/ai-studio/how-to/develop/flow-evaluate-sdk). Once you execute an evaluation run, you can [visualize the results in Azure AI Foundry portal ](https://learn.microsoft.com/azure/ai-studio/how-to/evaluate-flow-results).

lab/Skillable Workshop Instructions/00_Introduction.md

@@ -7,32 +7,27 @@
 
 By the end of this workshop, you should be able to:
 
- - Describe OpenAI model capabilities and implementation in the field of text generation, image generation, multimodal conversation, function calling and Azure OpenAI Assistants.
+ - Describe OpenAI model capabilities and implementation in the fields of text generation, image generation, multimodal conversation, and Azure OpenAI Assistants.
  - Understand prompt engineering concepts and best practices.
  - Leverage generative AI capabilities and apply prompt engineering techniques to your own solutions.
 
-## Lab Scenario
-
-You are a developer at *Contoso Outdoor Company*, a leading e-commerce company that sells outdoor gear and equipment. Your team is working on a new website design and you have been tasked with generating text content, images, and code snippets for the website. You have heard about the power of generative AI models and want to explore how you can leverage them to generate content for the website.
-
 ## Resources
 
 > [!TIP]
-> You can find login and subscription information over on the Resources tab
+> You can find login and subscription information over on the Resources tab.
 
 All additional information on the lab can be found on GitHub:
 
-- [Skillable Workshop Instructions](https://github.com/microsoft/aitour-interact-with-llms/blob/main/lab/Skillable%20Workshop%20Instructions/00_Introduction.md)
-- [Workshop Instructions if you are deploying directly on Azure](https://github.com/microsoft/aitour-interact-with-llms/blob/main/lab/Workshop%20Instructions/00_Introduction.md)
+- [Skillable Workshop Instructions](https://github.com/microsoft/aitour-interact-with-llms/tree/microsoft-ignite-update/lab/Skillable%20Workshop%20Instructions)
+<!-- - [Workshop Instructions if you are deploying directly on Azure](https://github.com/microsoft/aitour-interact-with-llms/blob/main/lab/Workshop%20Instructions/00_Introduction.md) -->
 
 ## Lab Outline
 
-The lab is organized into 5 sections, taking you through generating text content, image assets, and code snippets through a multimodal conversational interface on Azure with OpenAI models. In addition, we will cover Function Calling and AI Assistants. The goal of the lab is to leverage generative AI to build the user interface components for the *Contoso Outdoor Company* e-commerce website.
+The lab is organized into 4 sections, taking you through generating text content, image assets, and code snippets through a multimodal conversational interface on Azure with OpenAI models. In addition, we will cover AI Assistants. The goal of the lab is to leverage generative AI capabilities over a wide range of scenarios.
 
-1. **Part 1 - Text Generation** Generate text content and descriptions with GPT4-Turbo
+1. **Part 1 - Text Generation** Generate text content and descriptions with GPT4o
 2. **Part 2 - Image Generation** Generate image assets with DALLE-3
-3. **Part 3 - Multimodality** Leverage multimodal capabilities of GPT-4o to generate code snippets from hand-drawn sketches.
+3. **Part 3 - Multimodality** Leverage multimodal capabilities of GPT-4o to interact with images and text.
 4. **Part 4 - Azure AI Assistants** Use code interpreter to understand your data.
-5. **Part 5 - Function Calling** Generate structured outputs with GPT-4o.
 
 Click **Next** to set up your Workshop environment and get started.