Learning new Azure Skills using Bing AI

✅ I recently gained access to the new Bing AI Service – and decided to put it to the test for learning new skills, and testing out how it might impact some of the things I do regularly.



What is Bing AI?

Bing AI is the artificial intelligence technology that powers Bing search engine and its related products and services. Bing AI enables Bing to understand natural language queries, provide relevant and personalized results, generate rich and interactive content, and offer intelligent suggestions and insights. Bing AI is constantly learning and improving from billions of user interactions and feedback, as well as from the latest research and innovations in the field of AI. Bing AI aims to make search more helpful, engaging, and delightful for everyone.

⚠ But wait… I didn’t write that! I asked Bing AI to do it for me 😀:


How can we use this to learn?

Even in the short amount of time I’ve been able to use Bing AI, I can see it’s going to be a hugely powerful tool for learning, like having your own  instructor you can ask questions too when learning a new topic. I decided to put this to the test!

✅ I asked Bing AI to help me with a question I’d recently been discussing with a customer:

As you can see, the response here is clear and comprehensive – providing me with the information I requested, and also providing access to links that allow me to continue learning more about the topic. Let’s get more specific now… 

As you can see here, a comprehensive response is also provided – again with links and references to help me continue learning. Let’s try something else now – I want to try some code!

Let’s ask the same thing now, but with Terraform:

As you can see – I’m given two great examples I can deploy and test. An awesome way to learn and test out new things. But what if I am looking for advice…?

As you can see, again, we are given a comprehensive response that helps shape our decisions, and provides us with information and links. I then decided to be much more specific – asking for a code example for a very specific deployment. This is a great example of how AI based code creation can be used to significantly speed up code creation:

So this is looking great – let’s examine the code in more detail:

# Configure the Azure provider
provider "azurerm" {
  features {}
}

# Create a resource group
resource "azurerm_resource_group" "example" {
  name     = "example-rg"
  location = "eastus"
}

# Create a storage account
resource "azurerm_storage_account" "example" {
  name                     = "examplestorageaccount"
  resource_group_name      = azurerm_resource_group.example.name
  location                 = azurerm_resource_group.example.location
  account_tier             = "Standard"
  account_replication_type = "GZRS" # The most resilient type
}

# Create two storage containers
resource "azurerm_storage_container" "containerA" {
  name                  = "containerA"
  storage_account_name  = azurerm_storage_account.example.name
  container_access_type = "private"
}

resource "azurerm_storage_container" "containerB" {
  name                  = "containerB"
  storage_account_name  = azurerm_storage_account.example.name
  container_access_type = "private"
}

✅ Notice how there’s even a comment that GZRS is the most resilient type – exactly as I had requested!

❗ But wait… there’s a problem! If you’re familiar with Azure Storage, you’ll know that containers can only have lowercase alphanumeric characters, and “examplestorageaccount” is very likely to be in use already. Lets ask Bing AI to fix this!

I first asked for the Container names to be fixed:

The container names were then made lowercase:

# Create two storage containers
resource "azurerm_storage_container" "containera" {
  name                  = "containera"
  storage_account_name  = azurerm_storage_account.example.name
  container_access_type = "private"
}

resource "azurerm_storage_container" "containerb" {
  name                  = "containerb"
  storage_account_name  = azurerm_storage_account.example.name
  container_access_type = "private"
}

I then asked for the Storage Account Name to be made unique, here’s the code that was created:

# Create a random string to append to the storage account name
resource "random_string" "suffix" {
  length  = 8
  upper   = false
  special = false
}

# Create a storage account
resource "azurerm_storage_account" "example" {
  name                     = "example${random_string.suffix.result}"
  resource_group_name      = azurerm_resource_group.example.name
  location                 = azurerm_resource_group.example.location
  account_tier             = "Standard"
  account_replication_type = "GZRS" # The most resilient type
}

The final code created is shown below:

# Configure the Azure provider
provider "azurerm" {
  features {}
}

# Create a resource group
resource "azurerm_resource_group" "example" {
  name     = "example-rg"
  location = "eastus"
}

# Create a random string to append to the storage account name
resource "random_string" "suffix" {
  length  = 8
  upper   = false
  special = false
}

# Create a storage account
resource "azurerm_storage_account" "example" {
  name                     = "example${random_string.suffix.result}"
  resource_group_name      = azurerm_resource_group.example.name
  location                 = azurerm_resource_group.example.location
  account_tier             = "Standard"
  account_replication_type = "GZRS" # The most resilient type
}

# Create two storage containers
resource "azurerm_storage_container" "containera" {
  name                  = "containera"
  storage_account_name  = azurerm_storage_account.example.name
  container_access_type = "private"
}

resource "azurerm_storage_container" "containerb" {
  name                  = "containerb"
  storage_account_name  = azurerm_storage_account.example.name
  container_access_type = "private"
}

Before running the code (I already know this – but wanted to test Bing AI), I asked for help running it:

✅ Success – this code is valid and ready for deployment. I deployed this into my Lab for testing:

✅ As you can see, the deployment has worked. In this post so far I’ve asked for Tooling Comparison, questions around integration, Code Examples, Code Changes, and how I can deploy the code.

I also asked about training courses – and was given a comprehensive list, with links, of courses I could take to help further my learning:

As you can see, once again the results are great – exactly what I requested, with links and descriptions.


Conclusion

As you can see, Bing AI is a powerful tool that can help greatly when learning various subjects – I plan to do more testing and over the coming weeks, and I look forward to seeing this service develop! If you haven’t yet tried it – I would thoroughly recommend giving it a go 😀.

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