From a07e3fcc0c255396e9be3e52769797df7780fc66 Mon Sep 17 00:00:00 2001
From: suraj-only-git <141921174+suraj-only-git@users.noreply.github.com>
Date: Tue, 10 Sep 2024 22:39:33 +0530
Subject: [PATCH 1/3] Create masterdoc1.json
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+[ {
+ "Name": "Developing AI Applications with Azure AI Studio",
+ "Language": "English",
+ "Files": [
+ {
+ "RawFilePath": "https://raw.githubusercontent.com/suraj-only-git/Developing-AI-Applications-with-Azure-AI-Studio/main/Labs/getting-started.md",
+ "Order": 1
+ },
+ {
+ "RawFilePath": "https://raw.githubusercontent.com/CloudLabsAI-Azure/Developing-AI-Applications-with-Azure-AI-Studio/main/Labs/Exercise-02.md",
+ "Order": 2
+ }
+ ]
+} ]
From a3c44d965a567de0d5032dd29a6587de7c21587b Mon Sep 17 00:00:00 2001
From: suraj-only-git <141921174+suraj-only-git@users.noreply.github.com>
Date: Tue, 10 Sep 2024 22:40:31 +0530
Subject: [PATCH 2/3] Create ExerciseIBM-02.md
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+# Lab 01: Training the Model
+
+## Lab scenario
+
+In this lab, you will gain hands-on experience in initializing a Prompt Flow project in Azure AI Studio, setting up the necessary environment to begin developing, testing, and refining AI applications. You will create and customize prompts within Azure AI Studio's Prompt Flow. Starting with the creation of a new flow, you will add and configure the Prompt tool and develop a flow incorporating LLM (Large Language Model) and Prompt tools. By authoring a sample flow and running it with custom inputs, you'll learn how to monitor flow execution and evaluate outputs, thereby understanding the practical steps involved in developing, testing, and refining AI-driven workflows.
+## Lab objectives
+In this lab, you will perform the following:
+
+- Task 1 : Initialize a Prompt Flow Project
+- Task 2 : Create and Customize Prompts
+- Task 3 : Develop a Flow with LLM and Prompt Tools
+
+
+## Task 1: Initialize a Prompt Flow Project
+
+As involves setting up a structured environment to manage and streamline prompt-based AI tasks. This process typically includes creating a project directory, configuring necessary files and dependencies, and establishing a workflow for prompt design, testing, and iteration. By organizing prompts, data, and evaluation metrics in a centralized system, the project ensures consistent and efficient development, making it easier to refine prompts and achieve desired outcomes.
+
+1. Open a new tab, and navigate to the [Azure AI Studio](https://ai.azure.com/?reloadCount=1).
+
+ >**Note:** Select **Sign in**, if you are not signed in.
+
+1. On the **Azure AI Studio**, on the home page, select **+ New Project**.
+
+ 
+
+1. On the **Create a project** page, and follow these instructions to fill out the properties:
+
+ - Project name: **Modelproject-**
+ - Hub: Create a new hub
+ - Select **Next**.
+ - Hub name: **modelhub**
+ - Subscription: Set as default
+ - Resource group: **ODL-MEMT-**
+ - Location: ****
+ - Connect Azure AI Services or Azure OpenAI: Keep it as default
+ - Connect Azure AI Search: Keep it as default
+ - Select **Next**
+
+ 
+ 
+
+1. On the **Review and finish** page, select **Create a Project**.
+
+1. You will be able to track progress in resource creation, and the project will be created when the process is complete. Once a project is created, you can access the playground, tools, and other assets in the left navigation panel.
+
+> **Congratulations** on completing the task! Now, it's time to validate it. Here are the steps:
+> - If you receive a success message, you can proceed to the next task.
+> - If not, carefully read the error message and retry the step, following the instructions in the lab guide.
+> - If you need any assistance, please contact us at labs-support@spektrasystems.com. We are available 24/7 to help you out.
+
+
+### Task 2 : Create and Customize Prompts
+
+Creating and customizing prompts involves designing specific, targeted questions or statements to elicit desired responses or actions. This process includes defining clear objectives, understanding the audience, and using precise language to ensure clarity and relevance. Customization can further refine prompts to align with particular contexts or user needs, enhancing engagement and effectiveness in various applications such as education, customer service, and AI interactions.
+
+1. From the left navigation menu, under **Components**, select **Deployments (1)**.
+
+1. On the **Define an endpoint to deploy your model**, under **Model deployments** tab, select **+ Deploy base model (2)**.
+
+ 
+
+1. On the **Select a model** page, search and select **gpt-35-turbo (1)**, select **Confirm (3)** under the **gpt-35-turbo**.
+
+ 
+
+1. On **Deploy model gpt-35-turbo** follow these instructions to create the deployment:
+
+ - Deployment name: **gpt-35-turbo (1)**
+ - Model version: Keep it as default (2)
+ - Deployment type: **Standard (3)**
+ - Connected Azure OpenAI resource: Select your OpenAI resource (4)
+ - Tokens per Minute Rate Limit (thousands): **5K (5)**
+ - Content filter: Set as default (6)
+ - Enable dynamic quota: **Enabled (7)**
+ - Select **Deploy (8)**
+
+ 
+
+1. From the left navigation pane, select **Prompt flow (1)** > **+ Create (2)** to add the Prompt tool to your flow.
+
+ 
+
+1. On **Create a new flow** blade, under **Standard flow**, click on **Create (1)**, then enter **Promptflow- (2)** for Folder name, then click on **Create (3)**
+
+ 
+
+ >**Note:** If you encounter any errors, wait for 5 minutes and recreate the prompt flow with a unique name. Once it is created, rename the flow on the newly created prompt flow page to **Promptflow-**.
+
+> **Congratulations** on completing the task! Now, it's time to validate it. Here are the steps:
+> - If you receive a success message, you can proceed to the next task.
+> - If not, carefully read the error message and retry the step, following the instructions in the lab guide.
+> - If you need any assistance, please contact us at labs-support@spektrasystems.com. We are available 24/7 to help you out.
+
+
+### Task 3 : Develop a Flow with LLM and Prompt Tools
+
+Developing a flow with Large Language Models (LLMs) and prompt tools involves designing a structured interaction where the LLM is guided by carefully crafted prompts to generate desired outputs. This process typically includes defining the objective, selecting appropriate LLMs, and iteratively refining prompts based on the model's responses to ensure accuracy and relevance. Prompt tools assist in managing and optimizing this interaction, enabling more efficient and effective use of LLMs in tasks such as content creation, data analysis, and automated customer support.
+
+1. The prompt flow authoring page opens. You can start authoring your flow now. By default you see a sample flow. This example flow has nodes for the LLM and Python tools.
+
+1. Optionally, you can add more tools to the flow. The visible tool options are **LLM, Prompt, and Python**. To view more tools, select **+ More tools**.
+
+1. From the **Graph**, select **joke**. Choose an existing connection from the drop-down menu, and for deployment, select the newly created deployment, **gpt-35-turbo**, in the LLM tool editor.
+
+ 
+
+1. Scroll up for Input enter any fruit name as per your wish, select **Save**, and select **Start compute session**.
+
+ 
+
+ >**Note:** It will take 1 - 3 minutes to start the session.
+
+1. The flow run status is shown as Running, select **Run**.
+
+ 
+
+1. Once the flow run is completed, select View outputs to view the flow results. The output will look similar to the image as shown below.
+
+ 
+
+1. You can view the flow run status and output in the Outputs section.
+
+ 
+
+1. From the top menu, select **+ Prompt** to add the Prompt tool to your flow, give the name of the flow as **modelflow**, and select **Add**.
+
+ 
+
+1. Add this code inside the **modelflow** prompt tool, and select **Validate and parse input**
+
+ ```jinja
+ Welcome to {{ website_name }}!
+ {% if user_name %}
+ Hello, {{ user_name }}!
+ {% else %}
+ Hello there!
+ {% endif %}
+ Please select an option from the menu below:
+ 1. View your account
+ 2. Update personal information
+ 3. Browse available products
+ 4. Contact customer support
+ ```
+1. In the input section add these following value, select **Save** and **Run**.
+
+ - user_name: Jane
+ - website_name: Microsoft
+
+ 
+
+1. Once the flow run is completed, select View outputs to view the flow results. The output will look similar to the image as shown below.
+
+ 
+
+1. You can view the flow run status and output in the Outputs section.
+
+ 
+
+## Review
+In this lab you have completed the following tasks:
+
+- Initialize a Prompt Flow Project
+- Created and Customized Prompts
+- Developed a Flow with LLM and Prompt Tools
+
+### You have successfully completed the lab. Click on **Next >>** to procced with next exercise.
From 54023ff4e8e177f78ded870fb7d539c8c22931e3 Mon Sep 17 00:00:00 2001
From: suraj-only-git <141921174+suraj-only-git@users.noreply.github.com>
Date: Tue, 10 Sep 2024 22:40:53 +0530
Subject: [PATCH 3/3] Update masterdoc1.json
---
Labs/masterdoc1.json | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/Labs/masterdoc1.json b/Labs/masterdoc1.json
index 98aad5e..cd566df 100644
--- a/Labs/masterdoc1.json
+++ b/Labs/masterdoc1.json
@@ -7,7 +7,7 @@
"Order": 1
},
{
- "RawFilePath": "https://raw.githubusercontent.com/CloudLabsAI-Azure/Developing-AI-Applications-with-Azure-AI-Studio/main/Labs/Exercise-02.md",
+ "RawFilePath": "https://raw.githubusercontent.com/suraj-only-git/Developing-AI-Applications-with-Azure-AI-Studio/main/Labs/ExerciseIBM-02.md",
"Order": 2
}
]