Tag Archives: Azure OpenAI

Talk: Boston Code Camp 38 – Let’s Build a Goal-Oriented AI Agent Using Semantic Kernel

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29-Mar-2025

Today I attended and contributed a talk to the Boston Code Camp 38 (yes, impressively, the 38th edition of this event). I made the trip with Maura (she gave a talk combining Cryptocurrency And Agentic AI) and Kevin (he gave a talk on Top 10 AI Security Risks). and we got to hang out with and chat with so many cool people from the Boston technology community.

The description of my Let’s Build a Goal-Oriented AI Agent Using Semantic Kernel talk follows, followed by a couple of relevant links, then the slide deck.

Imagine an AI not limited to answering individual questions or chatting, but actively working towards a goal you’ve assigned to it.

In this session, we’ll explore the building of an AI Agent – an autonomous actor that can execute tasks and achieve objectives on your behalf.

Along the way we will demystify:

1. 🧠 LLMs – What is a Large Language Model (LLM)
2. 📚 Tokens – What is a token and what are its roles
3. 💡 Embeddings – What are embedding models and vectors and what can they do for us
4. 🎯 Prompts – Beyond the basics
5. ⚙️ Tools – How can these be created and accessed using Semantic Kernel
6. 🤖 Agents – Let’s put all these concepts to work!

The end result will be the core (or perhaps ‘kernel’ 😉) of an AI Agent – your virtual coworker willing to handle tasks on your behalf without. It will be built in C# using the open source, cross-platform Semantic Kernel library.

This talk assumes user-level familiarity with LLMs like ChatGPT or Microsoft Copilot and basic prompting. Anything else will be explained.

(stole photo from Robert Hurlbut)

A couple of prominent links from the talk are:

You can find the Fun with Vectors tool here: https://funwithvectors.com/

You can find the OpenAI Tokenizer tool here: https://platform.openai.com/tokenizer

Download the slides here:

BostonCodeCamp38-EssentialAgency-AGoalAndSomeTools-28-Mar-2025_pubDownload

Talk: AI for Application Architects

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Last night I was guest speaker at the Boston .NET Architecture community group. I learned they are now 21-year-olds. That’s a long track record! The audience had some insightful questions, which I always appreciate.

My talk focused on the perspective of the application architect – and not the data scientist, for example – in how the process works and what are some areas I would need to dig into.

Here’s the alternative talk description I offered a few days ago:

Interested in understanding how LLMs are created and how they work internally, including all the in-depth data science and machine learning techniques? If so, then this is not that talk. Rather, this talk steps back to treat the LLM as a black box. And then steps further back to treat the LLM as a part of a cohesive system offered over the internet through an API. It is from that perspective that we begin our exploration.

How exactly does an application make use of LLM services? Is this thing secure? Is it private? Am I operating according to Responsible AI principles? (Oh, and what are Responsible AI principles?) Is it accurate? Is it portable? And of course, when does it stop being a Chatbot and start being an Agent?

These are some of the key types of application architecture considerations we will discuss as we start with “the humble chatbot demo” then turn it into an Agent and then see what it would take to put that into production.

The deck is here:

Boston-.NET-Architecture–Architecting-AI-Applications-18-Dec-2024-publishedDownload

The recording is here: https://youtu.be/UJutO4eFLZg

Talk: Building an Agent with Semantic Kernel

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Today I attended and spoke at the 37th Boston Code Camp. The rainy weather was just enough to maximize attendance.

There was an incredibly energetic group of inquisitive people at my talk which was on how you can give your AI LLM a goal and some tools and let it figure out how to move ahead! Lots of questions came from this highly engaged group.

The details of my talk follow.

Building an AI Agent with Semantic Kernel

The classic approach to managing complexity is through abstraction. While also useful in the physical world (you can know how to use a “car” without needing to know about all the parts under the hood), it is an essential tool in software.

To program against the current generative AI models you can use the model’s native abstraction (their SDK). But there are other options too, one of which is to use Semantic Kernel, an open-source library from Microsoft.

In this talk we will understand the first-class abstractions representable using Semantic Kernel, from the granular Function and building up to an Agent, and a couple of steps in between.

This talk will be a mix of explaining AI-relevant and Semantic Kernel-relevant topics + some explanatory sample code. We may also sneak in a little Prompty.

By the end of this talk you will appreciate why you might (or might not) want to build your AI solution with Semantic Kernel (SK) and how you would approach it.

This talk will assume you have used LLMs (like ChatGPT or others) and know the very basics of iterating on prompts and experiencing that GenAI systems have an ability to make decisions from human language. Anything beyond this will be explained in the talk.

The sample application used in the talk can be found here:

https://github.com/semantickerneldev/icon-agent

The deck used in the talk can be found here:

AI-Agent-SemanticKernel-BostonCodeCamp-23-Nov-2024Download

Talk: Hello Semantic Kernel and Giving your AI a Goal

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At Virtual Boston Azure tonight Jason Haley and I teamed up to talk about ways Gen AI can be integrated with your existing systems. In the case of existing enterprise software systems, many are written in C# and Java, both languages supported by Semantic Kernel. Semantic Kernel also supports Python, which is a great language, but all things being equal using a language and technology stack already familiar to your team is also attractive. So considering a library like Semantic Kernel is a productive angle when looking across the spectrum of AI tools.

Much of my talk was focused on how to use Semantic Kernel (in C# and .NET 8) to give your AI a goal and have it solve it. The deck I presented and a recording of the talk follow. <I will likely update this post to link to code used in demo and as other artifacts become available>

Hello-SemanticKernel-BostonAzure-12-Nov-2024Download

hello-ai: A Simple Demonstration of Azure OpenAI

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I wrote some code demonstrating how to use Azure OpenAI to support the AI mini-workshop we ran for Virtual Boston Azure. I created versions in Python and C#.

This weekend I create a web front-end for it and deployed as an Azure Static Web App with an Azure Function supporting the refactored C# logic to execute the Azure OpenAI service calls.

The new app is running here: https://hello-ai.doingazure.com

You can find the source code here: https://github.com/codingoutloud/hello-ai

Note that while the additional grounding fails to stop all of the hallucinations, it does help with the most obvious one (so we are making progress) but there’s more to be done.

Workshop: AI Mini-Workshop at Boston Azure

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The March 28 Virtual Boston Azure was headlined by Pamela Fox from Microsoft. She explained all about the RAG pattern which is commonly used for building effective applications based on Large Language Models (“LLMs”) and Generative AI (“GenAI”). Pamela shared many superb insights, including lots of depth, while answering a ton of interesting follow-up questions. Was a fantastic talk. Boston Azure has a YouTube channel at youtube.com/bostonazure where you can find recordings of many past events. Pamela Fox’s talk is available there as the 48th video to be posted to the channel.

After Pamela’s talk around 15 people stuck around to participate in our first ever “AI mini-workshop” hands-on experience. The remainder of this post is about that mini-workshop.

The AI mini-workshop was a facilitated hands-on coding experience with the following goals:

1. Demystify Azure OpenAI

As background, OpenAI’s ChatGPT burst onto the scene in November 2022. That led to an explosion of people learning about AI and associated technologies such as “LLMs” which is the common shorthand for Large Language Models.

The vast majority of people interact with LLMs via chat interfaces such as available from OpenAI’s public version of ChatGPT or via Copilot on Microsoft Bing search. There’s also a more integrated programming experience surfaced through GitHub Copilot for use with VS Code and several other popular IDEs.

But what about programming your own solution that uses an LLM? Microsoft has done a great job of providing an enterprise-grade version of the OpenAI LLM as a set of services known as Azure OpenAI.

The first goal of this AI mini-workshop was to demystify this programming experience.

This was accomplished by giving the mini-workshop participants a working C# or Python program that fit on a page. And there are only around 10 lines of meaningful code needed to interact with the AI service. This is NOT that complex.

Creating a production-grade application has additional requirements, but at its core, it is straight-forward to interact with Azure OpenAI service programmatically.

The hoped for “Aha!” moment was this:

Aha #1! I can do this! I can programmatically interact with the Azure OpenAI LLM. It isn’t that mysterious after all.

Aha #2! This is possible without much code! In the Python and C# solutions shared there were only around 10 lines of core code.

2. Understand Some AI Concepts

Part of the mini-workshop exercise was to recognize a hallucination and fix it through some additional grounding using a very simple form of RAG.

The hope here is for some “Aha!” moments:

Aha #3! Here’s a concrete, understandable example of a hallucination!

Aha #4! And here’s a concrete, simple example use of RAG pattern to better ground the AI so that it no longer hallucinates about today’s date! But do note that other hallucinations remain…

3. Wield Great Power

The ability to program a LLM to generate unique content is something that essentially NO DEVELOPER COULD DO, EVER, before the super-powerful LLMs that were developed at costs of hundreds of millions of dollars and democratized by the Microsoft Azure OpenAI services (as well as by OpenAI themselves).

The hands-on AI mini-workshop required either (a) a functional Python 3 environment, or (b) a functional C#/.NET environment – everything else was provided, including sufficient access to the Azure OpenAI LLM service to complete the mini-workshop.

But in the end with very little coding you can get to the 5th Aha! moment which is:

Aha #5! I have at my command capabilities that have not been possible in all of the history of computers. The magic of LLMs available via Azure OpenAI gives me superpowers that we are only in the very beginning of understanding the ways this can be put to use.

The source code for the AI mini-working is available here. Note that the API key has subsequently been rolled (invalidated), but the code works pretty well otherwise. 🙂

My original thinking was to distribute the keys separately (like this). If this was an in-person workshop I would have kept the configuration values separated from the source, but given the added challenge of doing this with an online distributed audience I decided to simplify the mini workshop by included the configuration values directly in the source code. Looking back, I believe it was a good concession for minimizing obstacles to learning. So I’d do it again next time.

Talk: Boston Code Camp 36 – Meet GitHub Copilot, Your AI Coding Assistant!

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23-Mar-2024

Always great to hang out with the greater Boston tech community. Today I attended and contributed a talk to Boston Code Camp 36 (the 36th edition of this event).

I made the trip with Maura (she gave a talk on blockchain). and we met a lot of cool people and had a great time.

I spoke on GitHub Copilot. Much of my talk was demo and discussion – you have to see this in action (or use it) to appreciate what’s happening. I consider this a glimpse into the future – it will surely become then norm to have an AI assistant when programming.

It is fun have one AI 🤖 (GitHub Copilot) help us program another AI 🤖 (Azure OpenAI). 🤖 🤖 🤖 😀 After Copilot Chat was able to explain that Azure OpenAI did not have any notion of “today” we used Copilot to implement a trivial version of RAG to anchor the prompt to the current day.

We saw how the agents like @workspace can explain a body of code and even help us figure out where to implement a new feature (such as the --joke command line param).

Another demo was to get Copilot to write unit tests for me. The demo gods were not helpful 😱 😱 😱 and I ran into an error. I moved on without fixing it since time was short. I diagnosed it later and it turns out I had double-pasted (classic demo failure!) which caused the problem. We did use /tests to create unit tests, which were initially NUnit test, but then we asked Copilot to recast them as xUnit tests, then to more efficiently create test cases using the InlineData attribute to consolidate similar test cases.We didn’t get to run the tests at the end, but hopefully the power of GitHub Copilot in helping to create unit tests came through.

I also had the opportunity to hang out with some smart soon-to-be graduates from my alma mater – University of Massachusetts at Boston (some of them were Rohini Deshmukh, Master’s in Information Technology, Kunal Sahjwani, Master’s in Information Technology, and Shounak Kulkarni, Master’s in Business Analytics). Great to see our profession is in such capable hands from chatting with these very smart and capable technologists, analysts, and future leaders.

Here is the published talk abstract for the talk I delivered – and though much of the session was demos, the PowerPoint deck is attached after the abstract.

Meet GitHub Copilot, Your AI Coding Assistant

Imagine an assistant who anticipates your needs as you code, handling mundane and time-consuming steps, allowing you to focus on more complex challenges (the fun stuff). Allow me to introduce you to GitHub Copilot.

GitHub Copilot is an AI-powered coding assistant that integrates with your developer IDE adding many powerful productivity features. Backed by the same OpenAI Large Language Model (LLM) behind ChatGPT, it has an uncanny ability to suggest code snippets that you were about to type in. But suggesting code snippets is just the beginning.

In this demo-heavy talk, we’ll show usage basics, distinguish scenarios where it excels vs. some it finds challenging, and point out a few common anti-patterns so you can avoid them.

Since it is still early days, big features are still showing up at a fast clip, so we’ll highlight some recent features and some others just emerging. At the end we’ll squeeze in just a bit of prognosticating about what it might mean for the future of software development.

As you’ll learn in more depth during this session, the promise of GitHub Copilot is to help you be more productive – go faster, stay in flow, build even more robust software. We are not fully there but we are on the way. This imperfect tool is still a game changer.

I believe the rise of AI coding assistants is unstoppable at this point – and that’s a good thing. By the end of this session, you might agree. And maybe you’ll join the revolution early.

boston-codecamp-meet-github-copilot-22-mar-2024_pubDownload