Update 3-build-and-train-custom-ai-dialogue-models.md

Author: kprks

learn-pr/philanthropies/forging-voices-from-data/includes/3-build-and-train-custom-ai-dialogue-models.md

@@ -1,94 +1,81 @@
-## Build and Train Custom AI Dialogue Models
-
 Creating a custom dialogue system is about more than just picking a model — it’s about careful setup, thoughtful training, and ongoing improvement. In this lesson, you’ll walk through **five essential steps** for building and fine-tuning a functional AI-powered assistant using Azure OpenAI Studio. Each step builds technical skill while helping you shape AI behavior for your unique use case.
 
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-
-### Step 1: Set Up Your Azure OpenAI Studio Account
+## Step 1: Set up your Azure OpenAI Studio account
 
 Before you build, you need the right environment.
 
-- **Create an Azure Account**  
+1. **Create an Azure account**  
   Visit [azure.microsoft.com](https://azure.microsoft.com) to sign up or log in. An active subscription is required for Azure OpenAI resources.
 
-- **Access Azure OpenAI Studio**  
+2. **Access Azure OpenAI Studio**  
   Go to [Azure OpenAI Studio](https://oai.azure.com/) and confirm that OpenAI access has been approved for your account or organization.
 
-- **Set Up an OpenAI Resource**  
+3. **Set up an OpenAI resource**  
   In the Azure Portal, create a new OpenAI resource. Choose a region (like East US or West Europe) that aligns with your compliance needs.
 
 **Why it matters:** This setup ensures you’re working in a secure, scalable environment that can handle large language models.
 
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-### Step 2: Create a New Dialogue Model
+## Step 2: Create a new dialogue model
 
 With your setup complete, it’s time to build.
 
-- **Explore the Azure OpenAI Studio Dashboard**  
+1. **Explore the Azure OpenAI Studio dashboard**  
   This dashboard gives you access to models, playgrounds, deployments, and prompt configurations.
 
-- **Start a New Project**  
+2. **Start a new project**  
   Create a new deployment and select the right model (e.g., GPT-3 for general tasks or GPT-4 for more advanced dialogue).
 
-- **Define Your Model’s Purpose**  
+3. **Define your model’s purpose**  
   Clarify the assistant’s role:  
   Will it help users schedule meetings? Answer policy questions? Act as a digital concierge?
 
 **Why it matters:** A clear scope will help you design focused data inputs and prompt strategies.
 
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-
-### Step 3: Train the Model with Custom Data
+## Step 3: Train the model with custom data
 
 Training on your own data fine-tunes the assistant to your needs.
 
-- **Upload Relevant Data**  
+1. **Upload relevant data**  
   Provide chat logs, FAQs, product details, or service transcripts — real-world examples help the model learn.
 
-- **Fine-Tune with Example Pairs**  
+2. **Fine-tune with example pairs**  
   Use prompt engineering or fine-tuning to reinforce the patterns you want. Provide both user input and ideal output examples.
 
-- **Apply Supervised and Active Learning**  
+3. **Apply supervised and active learning**  
   Label outputs, collect feedback, and retrain as needed to improve accuracy over time.
 
 **Why it matters:** This step ensures your assistant isn’t just smart — it’s smart for your audience.
 
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-
-### Step 4: Customize Responses and Behavior
+## Step 4: Customize responses and behavior
 
 Shape your assistant’s tone, style, and personality.
 
-- **Set a System Prompt**  
+1. **Set a system prompt**  
   Provide instructions that guide all responses, like:  
   *“You are a friendly assistant who helps answer questions about university admissions.”*
 
-- **Manage Context and Memory**  
+2. **Manage context and memory**  
   Configure how the model handles follow-up questions. Use prompt chaining or summarized context to simulate conversation memory.
 
-- **Add Dynamic Variables**  
+3. **Add dynamic variables**  
   Use placeholders like `{user_name}` or `{appointment_time}` to personalize replies.
 
 **Why it matters:** Customization makes your assistant not just functional, but engaging and on-brand.
 
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-
-### Step 5: Test, Evaluate, and Iterate
+## Step 5: Test, evaluate, and iterate
 
 Great dialogue systems are built through continuous refinement.
 
-- **Run Simulated Scenarios**  
+1. **Run simulated scenarios**  
   Use the Azure chat playground to test real-world user cases. Identify strong points and gaps.
 
-- **Collect and Analyze Feedback**  
+2. **Collect and analyze feedback**  
   Monitor interactions to spot confusion or drop-offs. Gather direct feedback when possible.
 
-- **Refine and Redeploy Regularly**  
+3. **Refine and redeploy regularly**  
   Update prompts, retrain on new data, and redeploy improvements to keep your system sharp.
 
 **Why it matters:** Iteration is the key to long-term performance and reliability.
 
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-
 By following these five steps, you’ll move from **concept to custom-built AI assistant** — shaping not just what your system can do, but how well it connects with the people who use it.