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This Is What Azure OpenAI Taught Me

If you’re new to the Microsoft ecosystem, discussing the many theoretical solutions that are needed in an enterprise level environment and attempting to architect them in your head can be very overwhelming initially. Personally, I deal with this on a day-to-day basis in my current role. The good news is that this is something you can adapt to by building a mindset and framework that leverages AI, helping you handle these situations with greater confidence and efficiency.

I wanted to close that gap. So, I built a small tool that does the asking for me, and in the process, I learned more about how Azure Open AI actually works than any tutorial had taught me.

First, What Is Azure OpenAI

Azure OpenAI is just the AI models you already know in Chat GPT. With Azure Open AI, these models are running inside your Microsoft environment where your data stays yours. Think of it like this; Copilot Studio is like a finished car and Azure OpenAI is a standalone engine. Azure Open AI is a reusable service that has different AI models that you can leverage in your own, custom-built solutions based around AI models that you already use. It is a component or a plug that you can add to other things.

The idea: let AI do the first pass of discovery

The tool is called the Requirements Intake Analyzer. It’s about as simple as it sounds. You give it a random business request in plain English, it creates a structured breakdown for you:

  • What the business problem actually is
  • Who the stakeholders are
  • The hidden pain points
  • What can actually be automated
  • What information is missing
  • Follow-up questions you should be asking
  • Flagging any potential risks early on
  • A complexity rating, and which tools fit best

 

We all know that AI is a great tool for summarizing concepts. To unlock more potential and become a power user, there is a mental shift that causes more curiosity in allowing AI to detect the absence of very important pieces of the puzzle.

Here’s a real example business problem. The input was a marketing request that came in sounding reasonable enough:

“We need a Copilot Agent for the Marketing team to get ahead of decision making. We need triggers and alerts when competitors make website updates or show up in news headlines. We also need a weekly email summarizing major movers within our internal data.”

At first, this sounds like a pretty thoroughly described business problem that’s accurately portrayed in a solid prompt. Then the analyzer came back with a list of twelve things that had yet to be defined. Things like:

Which competitors? Which pages count? What separates a meaningful website change from a typo fix? What does “major mover” even mean in our internal data? Who receives the alerts? Are external news APIs already approved by security?

It rated the request to have high complexity and recommended prototyping with a single competitor and a single data source before building this out as a larger project.

Context like this can easily go undiscovered, leading to gaps in developing a solution.

Why Azure OpenAI, and why bother with Python

Of course, there are much simpler low-code tools, such as Copilot Studio, that let you build agents without writing a single line of code. That’s a huge advantage and makes AI development accessible for a wide range of use cases. However, when requirements become more complex, relying exclusively on low-code tools can sometimes limit how you think about solutions and grow your skills. Working only within a visual interface can create gaps in your understanding and may unintentionally narrow your view of what’s possible to build. Expanding beyond those tools can help unlock new approaches, deepen your technical thinking, and broaden the range of solutions you feel confident creating.
 

Building in Python and Azure OpenAI has forced me to see the entire end-to-end path starting with a request that gets wrapped in instructions, travels to a model that I deployed myself, and comes back as data I have to catch, clean, and use. Witnessing this all happen in your own terminal gives you a better understanding of this entire process and the setup is simpler than you may think.

  1. Deploy a model of your choice in Azure Open AI. This will be the engine.
  2. Store four values safely. Your endpoint, key, deployment name, and API version go in a .env file in VS Code, never hardcoded into your script. This is a very important habit to build early on.
  3. Write a system prompt. The prompt is what turns a regular chatbot into a solution discovery analyst with rules, a role, and a required output format.
  4. Ask for JSON. Instead of a paragraph, you get structured data you can use in other systems for a consistent data format they can use.

 

If You’re Reading This and Thinking “I’m not at this level yet”…..

Neither was I until recently. I don’t have a typical computer science background or degree in complex coding concepts and development. TechFluent helped me get here by building smaller things over time. The biggest lesson Azure OpenAI taught me wasn’t how to deploy a model or write Python code. It was how to think differently about solving problems. By using AI to uncover assumptions, identify missing requirements, and ask better questions, I found a way to navigate complex business challenges with more clarity and confidence.

And that is AI fluency in action.

Cristal Carter

Associate Developer, Collaboration Tools at Vital Farms

Tech Fluent alum.