Fine-Tune GPT-3.5-Turbo in Azure for Smarter AI Responses Part-1

5 min readFeb 10, 2025

In recent years, AI-powered language models have revolutionized the way businesses interact with users.offering impressive natural language understanding and generation capabilities. It is widely used for chatbots, content generation, customer support, and more.

However, while GPT-3.5-Turbo is highly versatile, its default responses may not always align with specific business needs or domain-specific requirements. This is where fine-tuning comes in.

Why Fine-Tune GPT models?

Fine-tuning allows you to customize the model’s behavior by training it with your own dataset, leading to:
✅ More relevant responses — Tailor the model to industry-specific language and use cases.
✅ Improved accuracy — Reduce incorrect or generic responses by feeding domain-specific knowledge.
✅ Efficiency gains — Decrease token usage by making responses more precise, potentially reducing API costs.

Step 1: Deploying the GPT-3.5-Turbo Model and Retrieving API Credentials

Before fine-tuning, you need to deploy a GPT model in Azure OpenAI and obtain the API key and endpoint for interaction.

For this demonstration, I am using GPT-3.5-Turbo, but you can choose from the available models in Azure OpenAI

Not all GPT models in Azure OpenAI support fine-tuning. As of now, only specific models can be fine-tuned, while others are available for inference only.

To check the latest list of fine-tunable models, refer to the official Azure OpenAI documentation: Azure OpenAI Service Model Documentation

Steps to Deploy and Retrieve API Credentials:

Before we can fine-tune GPT-3.5-Turbo, we need to set up an Azure OpenAI account and deploy the base model. Follow these steps to get started:

Navigate to the Deployments menu in the Azure OpenAI Studio.Under Shared Deployments, locate the gpt-35-turbo model deployed in your Azure OpenAI account

Click on the model to open its details.
Copy the API Key and Target URI (Endpoint) — these will be required for interacting with the modeL.

Preparing the Dataset for Fine-Tuning:

Now that your GPT model is deployed in Azure, you’re ready to fine-tune it. Fine-tuning requires a dataset in JSONL format (JSON Lines), where each line represents a structured conversation between the user and the model.

Let’s create a few sample data points focused on software engineering concepts.

{"messages": [{"role": "system", "content": "You are an AI that provides clear, structured coding explanations."}, {"role": "user", "content": "How do I create a table in MySQL?"}, {"role": "assistant", "content": "To create a table in MySQL, use the `CREATE TABLE` statement:\n```sql\nCREATE TABLE users (\n  id INT AUTO_INCREMENT PRIMARY KEY,\n  name VARCHAR(100),\n  email VARCHAR(255) UNIQUE,\n  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n```\nThis defines a `users` table with an auto-incrementing ID and a timestamp."}]}

{"messages": [{"role": "system", "content": "You explain technical concepts in simple terms."}, {"role": "user", "content": "What is an API?"}, {"role": "assistant", "content": "An API (Application Programming Interface) allows two systems to communicate. Imagine a restaurant:\n- **You (Client):** Order food.\n- **Waiter (API):** Delivers request to the kitchen.\n- **Kitchen (Server):** Prepares and returns food.\nExample API request:\n```http\nGET https://api.example.com/users\n```\nResponse:\n```json\n{\n  \"id\": 1,\n  \"name\": \"John Doe\"\n}\n```"}]}

{"messages": [{"role": "system", "content": "You are an AI specializing in IoT and cloud integration."}, {"role": "user", "content": "How do I send sensor data to Azure IoT Hub?"}, {"role": "assistant", "content": "To send sensor data to Azure IoT Hub, use Python:\n```python\nfrom azure.iot.device import IoTHubDeviceClient, Message\nclient = IoTHubDeviceClient.create_from_connection_string('YOUR_CONNECTION_STRING')\ndata = '{\"temperature\": 25, \"humidity\": 60}'\nclient.send_message(Message(data))\nclient.disconnect()\n```\nThis securely transmits real-time data from IoT sensors."}]}

{"messages": [{"role": "system", "content": "You provide security best practices in API development."}, {"role": "user", "content": "How can I secure an API?"}, {"role": "assistant", "content": "To secure an API:\n1. **Use Authentication** (OAuth, JWT).\n2. **Enforce HTTPS** to encrypt communication.\n3. **Rate Limit Requests** to prevent abuse.\n4. **Validate User Inputs** to avoid SQL injection.\n5. **Monitor Logs** to detect unusual activities."}]}

{"messages": [{"role": "system", "content": "You optimize SQL queries and database performance."}, {"role": "user", "content": "How do I speed up a slow SQL query?"}, {"role": "assistant", "content": "To improve SQL query performance:\n1. **Index frequently queried columns.**\n2. **Avoid SELECT ***; fetch only required columns.\n3. **Use EXPLAIN ANALYZE** to identify slow parts.\n4. **Optimize joins** by indexing foreign keys.\n5. **Partition large tables** for better performance."}]}

{"messages": [{"role": "system", "content": "You are an expert in Python development and best practices."}, {"role": "user", "content": "How do I write efficient Python code?"}, {"role": "assistant", "content": "Follow these best practices:\n1. **Use List Comprehensions** for faster loops.\n2. **Avoid global variables** to optimize memory usage.\n3. **Use f-strings** instead of `+` for string formatting.\n4. **Leverage built-in functions** like `sum()` instead of loops.\n5. **Profile code** with `cProfile` to detect bottlenecks."}]}

{"messages": [{"role": "system", "content": "You provide guidance on cloud deployments."}, {"role": "user", "content": "How can I deploy a Flask app on Azure?"}, {"role": "assistant", "content": "To deploy a Flask app on Azure:\n1. Install Azure CLI and login:\n```sh\naz login\n```\n2. Create an Azure App Service:\n```sh\naz webapp create --resource-group myGroup --plan myPlan --name myFlaskApp --runtime 'PYTHON:3.9'\n```\n3. Deploy using Git or ZIP."}]}

{"messages": [{"role": "system", "content": "You are an AI that provides best practices for AI and ML development."}, {"role": "user", "content": "How do I fine-tune GPT-3.5?"}, {"role": "assistant", "content": "To fine-tune GPT-3.5:\n1. **Prepare JSONL data**:\n```json\n{\"messages\": [{\"role\": \"system\", \"content\": \"Instruction\"}, {\"role\": \"user\", \"content\": \"Query\"}, {\"role\": \"assistant\", \"content\": \"Response\"}]}\n```\n2. **Upload dataset to Azure Blob Storage**.\n3. **Start fine-tuning in Azure OpenAI Studio**.\n4. **Test responses with your fine-tuned model**."}]}

{"messages": [{"role": "system", "content": "You provide beginner-friendly explanations."}, {"role": "user", "content": "What is Machine Learning?"}, {"role": "assistant", "content": "Machine Learning (ML) enables computers to learn from data without explicit programming. Example:\n- **Input:** Email data (spam or not spam).\n- **Model:** Learns patterns.\n- **Output:** Predicts if new emails are spam.\nCommon ML algorithms:\n- **Linear Regression** (for predictions)\n- **Decision Trees** (for classifications)\n- **Neural Networks** (for deep learning)"}]}

{"messages": [{"role": "system", "content": "You are an AI that provides best practices in React development."}, {"role": "user", "content": "What are best practices for React performance?"}, {"role": "assistant", "content": "Improve React performance by:\n1. **Using React.memo()** to prevent unnecessary re-renders.\n2. **Lazy-loading components** with `React.lazy()`.\n3. **Optimizing re-renders** with `useCallback` and `useMemo`.\n4. **Minimizing state updates** to reduce unnecessary renders.\n5. **Using Virtualization** (e.g., react-window) for large lists."}]}

Dataset Best Practices:

✅ Use clear, well-structured question-answer pairs.
✅ Include varied examples to improve response quality.
✅ Keep the dataset concise yet informative to reduce fine-tuning costs.

Once you’ve created a dataset, save it as dataset.jsonl. Next, we'll upload it to Azure and start the fine-tuning process. 🚀

🚀 Coming Up Next: Fine-Tuning in Action!

This is just the beginning! In Part 2, we’ll dive into the actual fine-tuning process, including uploading the dataset, starting the fine-tuning job, and deploying your custom model in Azure. Stay tuned! 🎯