Build a smart shopping AI Agent with memory using the Azure AI Foundry Agent service
When we think about human intelligence, memory is one of the first things that comes to mind. It’s what enables us to learn from our experiences, adapt to new situations, and make more informed decisions over time. Similarly, AI Agents become smarter with memory. For example, an agent can remember your past purchases, your budget, your preferences, and suggest gifts for your friends based on the learning from the past conversations. Agents usually break tasks into steps (plan → search → call API → parse → write), but then they might forget what happened in earlier steps without memory. Agents repeat tool calls, fetch the same data again, or miss simple rules like “always refer to the user by their name.” As a result of repeating the same context over and over again, the agents can spend more tokens, achieve slower results, and provide inconsistent answers. You can read my other article about why memory is important for AI Agents. In this article, we’ll explore why memory is so important for AI Agents and walk through an example of a Smart Shopping Assistant to see how memory makes it more helpful and personalized. You will learn how to integrate Memori with the Azure AI Foundry AI Agent service. Smart Shopping Experience With Memory for an AI Agent This demo showcases an Agent that remembers customer preferences, shopping behavior, and purchase history to deliver personalized recommendations and experiences. The demo walks through five shopping scenarios where the assistant remembers customer preferences, budgets, and past purchases to give personalized recommendations. From buying Apple products and work setups to gifts, home needs, and books, the assistant adapts to each need and suggests complementary options. Learns Customer Preferences: Remembers past purchases and preferences Provides Personalized Recommendations: Suggests products based on shopping history Budget-Aware Shopping: Considers customer budget constraints Cross-Category Intelligence: Connects purchases across different product categories Gift Recommendations: Suggests gifts based on the customer's history Contextual Conversations: Maintains shopping context across interactions Check the GitHub repo with the full agent source code and try out the live demo. How Smart Shopping Assistant Works We use the Azure AI Foundry Agent Service to build the shopping assistant and added Memori, an open-source memory solution, to give it persistent memory. You can check out the Memori GitHub repo here: https://github.com/GibsonAI/memori. We connect Memori to a local SQLite database, so the assistant can store and recall information. You can also use any other relational databases like PostgreSQL or MySQL. Note that it is a simplified version of the actual smart shopping assistant implementation. Check out the GitHub repo code for the full version. from azure.ai.agents.models import FunctionTool from azure.ai.projects import AIProjectClient from azure.identity import DefaultAzureCredential from dotenv import load_dotenv from memori import Memori, create_memory_tool # Constants DATABASE_PATH = "sqlite:///smart_shopping_memory.db" NAMESPACE = "smart_shopping_assistant" # Create Azure provider configuration for Memori azure_provider = ProviderConfig.from_azure( api_key=os.environ["AZURE_OPENAI_API_KEY"], azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"], azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], api_version=os.environ["AZURE_OPENAI_API_VERSION"], model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], ) # Initialize Memori for persistent memory memory_system = Memori( database_connect=DATABASE_PATH, conscious_ingest=True, auto_ingest=True, verbose=False, provider_config=azure_provider, namespace=NAMESPACE, ) # Enable the memory system memory_system.enable() # Create memory tool for agents memory_tool = create_memory_tool(memory_system) def search_memory(query: str) -> str: """Search customer's shopping history and preferences""" try: if not query.strip(): return json.dumps({"error": "Please provide a search query"}) result = memory_tool.execute(query=query.strip()) memory_result = ( str(result) if result else "No relevant shopping history found" ) return json.dumps( { "shopping_history": memory_result, "search_query": query, "timestamp": datetime.now().isoformat(), } ) except Exception as e: return json.dumps({"error": f"Memory search error: {str(e)}"}) ... This setup records every conversation, and user preferences are saved under a namespace called smart_shopping_assistant. We plug Memori into the Azure AI Foundry agent as a function tool. The agent can call search_memory() to look at the shopping history each time. ... functions = FunctionTool(search_memory) # Get configuration from environment project_endpoint = os.environ["PROJECT_ENDPOINT"] model_name = os.environ["MODEL_DEPLOYMENT_NAME"] # Initialize the AIProjectClient project_client = AIProjectClient( endpoint=project_endpoint, credential=DefaultAzureCredential() ) print("Creating Smart Shopping Assistant...") instructions = """You are an advanced AI shopping assistant with memory capabilities. You help customers find products, remember their preferences, track purchase history, and provide personalized recommendations. """ agent = project_client.agents.create_agent( model=model_name, name="smart-shopping-assistant", instructions=instructions, tools=functions.definitions, ) thread = project_client.agents.threads.create() print(f"Created shopping assistant with ID: {agent.id}") print(f"Created thread with ID: {thread.id}") ... This integration makes the Azure-powered agent memory-aware: it can search customer history, remember preferences, and use that knowledge when responding. Setting Up and Running AI Foundry Agent with Memory Go to the Azure AI Foundry portal and create a project by following the guide in the Microsoft docs. Deploy a model like GPT-4o. You will need the Project Endpoint and Model Deployment Name to run the example. 1. Before running the demo, install the required libraries: pip install memorisdk azure-ai-projects azure-identity python-dotenv 2. Set your Azure environment variables: # Azure AI Foundry Project Configuration export PROJECT_ENDPOINT="https://your-project.eastus2.ai.azure.com" # Azure OpenAI Configuration export AZURE_OPENAI_API_KEY="your-azure-openai-api-key-here" export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/" export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o" export AZURE_OPENAI_API_VERSION="2024-12-01-preview" 3. Run the demo: python smart_shopping_demo.py The script runs predefined conversations to show how the assistant works in real life. Example: Hi! I'm looking for a new smartphone. I prefer Apple products and my budget is around $1000. The assistant responds by considering previous preferences, suggesting iPhone 15 Pro and accessories, and remembering your price preference for the future. So next time, it might suggest AirPods Pro too. The assistant responds by considering previous preferences, suggesting iPhone 15 Pro and accessories, and remembering your price preference for the future. So next time, it might suggest AirPods Pro too. How Memori Helps Memori decides which long-term memories are important enough to promote into short-term memory, so agents always have the right context at the right time. Memori adds powerful memory features for AI Agents: Structured memory: Learns and validates preferences using Pydantic-based logic Short-term vs. long-term memory: You decide what’s important to keep Multi-agent memory: Shared knowledge between different agents Automatic conversation recording: Just one line of code Multi-tenancy: Achieved with namespaces, so you can handle many users in the same setup. What You Can Build with This You can customize the demo further by: Expanding the product catalog with real inventory and categories that matter to your store. Adding new tools like “track my order,” “compare two products,” or “alert me when the price drops.” Connecting to a real store API (Shopify, WooCommerce, Magento, or a custom backend) so recommendations are instantly shoppable. Enabling cross-device memory, so the assistant remembers the same user whether they’re on web, mobile, or even a voice assistant. Integrating with payment and delivery services, letting users complete purchases right inside the conversation. Final Thoughts AI agents become truly useful when they can remember. With Memori + Azure AI Founder, you can build assistants that learn from each interaction, gets smarter over time, and deliver delightful, personal experiences.GPT-5 Family of Models & GPT OSS Are Now Available in AI Toolkit for VS Code
AI Toolkit v0.18.3 is here—and it’s a major milestone. This release introduces full support for: The latest GPT-5 family of models OpenAI’s open-source models (GPT OSS) via Azure AI Foundry and ONNX Runtime Whether you're building in the cloud, on the edge, or experimenting locally, this update gives you more flexibility, performance, and choice than ever.Impariamo a conoscere MCP: Introduzione al Model Context Protocol (MCP)
Non perderti il prossimo evento “Let’s Learn – MCP” su Microsoft Reactor il 24 di Luglio, pensato per chiunque voglia conoscere meglio il nuovo standard per agenti intelligenti (il Model Context Protocol) e imparare a metterlo in pratica. La sessione è in Italiano e le demo sono in Python, ma fa parte di una serie di live-streaming disponibili in tantissime lingue.How do I give my agent access to tools?
Welcome back to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a real question from the community with simple, practical guidance to help you build smarter agents. Today’s question comes from someone trying to move beyond chat-only agents into more capable, action-driven ones: 💬 Dear Agent Support I want my agent to do more than just respond with text. Ideally, it could look up information, call APIs, or even run code—but I’m not sure where to start. How do I give my agent access to tools? This is exactly where agents start to get interesting! Giving your agent tools is one of the most powerful ways to expand what it can do. But before we get into the “how,” let’s talk about what tools actually mean in this context—and how Model Context Protocol (MCP) helps you use them in a standardized, agent-friendly way. 🛠️ What Do We Mean by “Tools”? In agent terms, a tool is any external function or capability your agent can use to complete a task. That might be: A web search function A weather lookup API A calculator A database query A custom Python script When you give an agent tools, you’re giving it a way to take action—not just generate text. Think of tools as buttons the agent can press to interact with the outside world. ⚡ Why Give Your Agent Tools? Without tools, an agent is limited to what it “knows” from its training data and prompt. It can guess, summarize, and predict, but it can’t do. Tools change that! With the right tools, your agent can: Pull live data from external systems Perform dynamic calculations Trigger workflows in real time Make decisions based on changing conditions It’s the difference between an assistant that can answer trivia questions vs. one that can book your travel or manage your calendar. 🧩 So… How Does This Work? Enter Model Context Protocol (MCP). MCP is a simple, open protocol that helps agents use tools in a consistent way—whether those tools live in your app, your cloud project, or on a server you built yourself. Here’s what MCP does: Describes tools in a standardized format that models can understand Wraps the function call + input + output into a predictable schema Lets agents request tools as needed (with reasoning behind their choices) This makes it much easier to plug tools into your agent workflow without reinventing the wheel every time! 🔌 How to Connect an Agent to Tools Wiring tools into your agent might sound complex, but it doesn’t have to be! If you’ve already got a MCP server in mind, there’s a straightforward way within the AI Toolkit to expose it as a tool your agent can use. Here’s how to do it: Open the Agent Builder from the AI Toolkit panel in Visual Studio Code. Click the + New Agent button and provide a name for your agent. Select a Model for your agent. Within the Tools section, click + MCP Server. In the wizard that appears, click + Add Server. From there, you can select one of the MCP servers built my Microsoft, connect to an existing server that’s running, or even create your own using a template! After giving the server a Server ID, you’ll be given the option to select which tools from the server to add for your agent. Once connected, your agent can call tools dynamically based on the task at hand. 🧪 Test Before You Build Once you’ve connected your agent to an MCP server and added tools, don’t jump straight into full integration. It’s worth taking time to test whether the agent is calling the right tool for the job. You can do this directly in the Agent Builder: enter a test prompt that should trigger a tool in the User Prompt field, click Run, and observe how the model responds. This gives you a quick read on tool call accuracy. If the agent selects the wrong tool, it’s a sign that your system prompt might need tweaking before you move forward. However, if the agent calls the correct tool but the output still doesn’t look right, take a step back and check both sides of the interaction. It might be that the system prompt isn’t clearly guiding the agent on how to use or interpret the tool’s response. But it could also be an issue with the tool itself—whether that’s a bug in the logic, unexpected behavior, or a mismatch between input and expected output. Testing both the tool and the prompt in isolation can help you pinpoint where things are going wrong before you move on to full integration. 🔁 Recap Here’s a quick rundown of what we covered: Tools = external functions your agent can use to take action MCP = a protocol that helps your agent discover and use those tools reliably If the agent calls the wrong tool—or uses the right tool incorrectly—check your system prompt and test the tool logic separately to isolate the issue. 📺 Want to Go Deeper? Check out my latest video on how to connect your agent to a MCP server—it’s part of the Build an Agent Series, where I walk through the building blocks of turning an idea into a working AI agent. The MCP for Beginners curriculum covers all the essentials—MCP architecture, creating and debugging servers, and best practices for developing, testing, and deploying MCP servers and features in production environments. It also includes several hands-on exercises across .NET, Java, TypeScript, JavaScript and Python. 👉 Explore the full curriculum: aka.ms/AITKmcp And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! Whether you’re building a productivity agent, a data assistant, or a game bot—tools are how you turn your agent from smart to useful.AI Toolkit for VS Code July Update
We’re back with the July update for the AI Toolkit for Visual Studio Code! This month marks a major release—version 0.16.0—bringing features we teased in the June prerelease into full availability, along with new enhancements across the board. 💳 GitHub Pay-as-you-go Model Support AI Toolkit now supports GitHub pay-as-you-go models, making it easier to continue building without friction when you exceed the free tier limits. Here’s how it works: When you hit the usage limit for GitHub’s hosted models, AI Toolkit will provide a clear prompt with a link to GitHub’s paid usage documentation. You can enable model usage billing in your GitHub Settings. Once enabled, you can continue using the same model in the Playground or Agent Builder—no changes needed in your workflow. This integration helps keep your development flow uninterrupted while giving you flexibility on how and when to scale. 🤖 Agent Builder Enhancements In last month’s June update, we introduced early access to function calling and agent version management—two highly requested features for building more reliable and dynamic AI agents. With v0.16.0, these features are now officially included in the stable release. We’ve also made several usability improvements: Resizable divider between input and output panels Cleaner, more compact evaluation results table Explore the latest features in the Agent Builder documentation. 🌐 Agent Development in VS Code: From Local to Cloud Whether you’re just getting started or ready to scale, AI Toolkit now makes it seamless to go from local experiments to production-ready deployments in the cloud. 🔍 Discover and Deploy Models with Ease You can now browse and explore a growing collection of models—including 100+ models from Azure AI Foundry—directly in the Model Catalog view. Once you’ve selected a model, you can deploy it to Azure AI Foundry with a single click, all within VS Code. ➡️ Learn more about the Model Catalog ➡️ How deployment works in Azure AI Foundry ⚙️ Iterate Locally, Scale When Ready The Playground and Agent Builder experience is designed to help you with prototyping quickly. Start with free, hosted models on GitHub to experiment with prompts and agent behavior. As your use case grows, you might hit rate limits or token caps—when that happens, the toolkit will proactively guide you to deploy the same model to Azure AI Foundry, so you can continue testing without interruption. 🚀 Get Started and Share Your Feedback If you haven’t tried the AI Toolkit for VS Code yet, now is a great time to get started. Install it directly from the Visual Studio Code Marketplace and begin building intelligent agents today. We’d love to hear from you! Whether it's a feature request, a bug report, or feedback on your experience, join the conversation and contribute directly on our GitHub repository. Let’s build the future of AI agent development together 💡💬 📄 See full changelog for v0.16.0 🧠 AI Toolkit Documentation Happy coding, and see you in August!JS AI Build‑a‑thon: Wrapping Up an Epic June 2025!
After weeks of building, testing, and learning — we’re officially wrapping up the first-ever JS AI Build-a-thon 🎉. This wasn't your average coding challenge. This was a hands-on journey where JavaScript and TypeScript developers dove deep into real-world AI concepts — from local GenAI prototyping to building intelligent agents and deploying production-ready apps. Whether you joined from the start or hopped on midway, you built something that matters — and that’s worth celebrating. Replay the Journey No worries if you joined late or want to revisit any part of the journey. The JS AI Build-a-thon was designed to let you learn at your own pace, so whether you're starting now or polishing up your final project, here’s your complete quest map: Build-a-thon set up guide: https://aka.ms/JSAIBuildathonSetup Quest 1: 🔧 Build your first GenAI app locally with GitHub Models 👉🏽 https://aka.ms/JSAIBuildathonQuest1 Quest 2: ☁️ Move your AI prototype to Azure AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest Quest 3: 🎨 Add a chat UI using Vite + Lit 👉🏽 https://aka.ms/JSAIBuildathonQuest3 Quest 4: 📄 Enhance your app with RAG (Chat with Your Data) 👉🏽 https://aka.ms/JSAIBuildathonQuest4 Quest 5: 🧠 Add memory and context to your AI app 👉🏽 https://aka.ms/JSAIBuildathonQuest5 Quest 6: ⚙️ Build your first AI Agent using AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest6 Quest 7: 🧩 Equip your agent with tools from an MCP server 👉🏽 https://aka.ms/JSAIBuildathonQuest7 Quest 8: 💬 Ground your agent with real-time search using Bing 👉🏽 https://aka.ms/JSAIBuildathonQuest8 Quest 9: 🚀 Build a real-world AI project with full-stack templates 👉🏽 https://aka.ms/JSAIBuildathonQuest9 Link to our space in the AI Discord Community: https://aka.ms/JSAIonDiscord Project Submission Guidelines 📌 Quest 9 is where it all comes together. Participants chose a problem, picked a template, customized it, submitted it, and rallied their community for support! 🏅 Claim Your Badge! Whether you completed select quests or went all the way, we celebrate your learning. If you participated in the June 2025 JS AI Build-a-thon, make sure to Submit the Participation Form to receive your participation badge recognizing your commitment to upskilling in AI with JavaScript/ TypeScript. What’s Next? We’re not done. In fact, we’re just getting started. We’re already cooking up JS AI Build-a-thon v2, which will introduce: Running everything locally with Foundry Local Real-world RAG with vector databases Advanced agent patterns with remote MCPs And much more based on your feedback Want to shape what comes next? Drop your ideas in the participation form and in our Discord. In the meantime, add these resources to your JavaScript + AI Dev Pack: 🔗 Microsoft for JavaScript developers 📚 Generative AI for Beginners with JavaScript Wrap-Up This build-a-thon showed what’s possible when developers are empowered to learn by doing. You didn’t just follow tutorials — you shipped features, connected services, and created working AI experiences. We can’t wait to see what you build next. 👉 Bookmark the repo 👉 Join the community on Join the Azure AI Foundry Discord Server! 👉 Stay building Until next time — keep coding, keep shipping!AI Repo of the Week: Generative AI for Beginners with JavaScript
Introduction Ready to explore the fascinating world of Generative AI using your JavaScript skills? This week’s featured repository, Generative AI for Beginners with JavaScript, is your launchpad into the future of application development. Whether you're just starting out or looking to expand your AI toolbox, this open-source GitHub resource offers a rich, hands-on journey. It includes interactive lessons, quizzes, and even time-travel storytelling featuring historical legends like Leonardo da Vinci and Ada Lovelace. Each chapter combines narrative-driven learning with practical exercises, helping you understand foundational AI concepts and apply them directly in code. It’s immersive, educational, and genuinely fun. What You'll Learn 1. 🧠 Foundations of Generative AI and LLMs Start with the basics: What is generative AI? How do large language models (LLMs) work? This chapter lays the groundwork for how these technologies are transforming JavaScript development. 2. 🚀 Build Your First AI-Powered App Walk through setting up your environment and creating your first AI app. Learn how to configure prompts and unlock the potential of AI in your own projects. 3. 🎯 Prompt Engineering Essentials Get hands-on with prompt engineering techniques that shape how AI models respond. Explore strategies for crafting prompts that are clear, targeted, and effective. 4. 📦 Structured Output with JSON Learn how to guide the model to return structured data formats like JSON—critical for integrating AI into real-world applications. 5. 🔍 Retrieval-Augmented Generation (RAG) Go beyond static prompts by combining LLMs with external data sources. Discover how RAG lets your app pull in live, contextual information for more intelligent results. 6. 🛠️ Function Calling and Tool Use Give your LLM new powers! Learn how to connect your own functions and tools to your app, enabling more dynamic and actionable AI interactions. 7. 📚 Model Context Protocol (MCP) Dive into MCP, a new standard for organizing prompts, tools, and resources. Learn how it simplifies AI app development and fosters consistency across projects. 8. ⚙️ Enhancing MCP Clients with LLMs Build on what you’ve learned by integrating LLMs directly into your MCP clients. See how to make them smarter, faster, and more helpful. ✨ More chapters coming soon—watch the repo for updates! Companion App: Interact with History Experience the power of generative AI in action through the companion web app—where you can chat with historical figures and witness how JavaScript brings AI to life in real time. Conclusion Generative AI for Beginners with JavaScript is more than a course—it’s an adventure into how storytelling, coding, and AI can come together to create something fun and educational. Whether you're here to upskill, experiment, or build the next big thing, this repository is your all-in-one resource to get started with confidence. 🔗 Jump into the future of development—check out the repo and start building with AI today!I want to show my agent a picture—Can I?
Welcome to Agent Support—a developer advice column for those head-scratching moments when you’re building an AI agent! Each post answers a question inspired by real conversations in the AI developer community, offering practical advice and tips. To kick things off, we’re tackling a common challenge for anyone experimenting with multimodal agents: working with image input. Let’s dive in! Dear Agent Support, I’m building an AI agent, and I’d like to include screenshots or product photos as part of the input. But I’m not sure if that’s even possible, or if I need to use a different kind of model altogether. Can I actually upload an image and have the agent process it? Great question, and one that trips up a lot of people early on! The short answer is: yes, some models can process images—but not all of them. Let’s break that down a bit. 🧠 Understanding Image Input When we talk about image input or image attachments, we’re talking about the ability to send a non-text file (like a .png, .jpg, or screenshot) into your prompt and have the model analyze or interpret it. That could mean describing what’s in the image, extracting text from it, answering questions about a chart, or giving feedback on a design layout. 🚫 Not All Models Support Image Input That said, this isn’t something every model can do. Most base language models are trained on text data only, they’re not designed to interpret non-text inputs like images. In most tools and interfaces, the option to upload an image only appears if the selected model supports it, since platforms typically hide or disable features that aren't compatible with a model's capabilities. So, if your current chat interface doesn’t mention anything about vision or image input, it’s likely because the model itself isn’t equipped to handle it. That’s where multimodal models come in. These are models that have been trained (or extended) to understand both text and images, and sometimes other data types too. Think of them as being fluent in more than one language, except in this case, one of those “languages” is visual. 🔎 How to Find Image-Supporting Models If you’re trying to figure out which models support images, the AI Toolkit is a great place to start! The extension includes a built-in Model Catalog where you can filter models by Feature—like Image Attachment—so you can skip the guesswork. Here’s how to do it: Open the Model Catalog from the AI Toolkit panel in Visual Studio Code. Click the Feature filter near the search bar. Select Image Attachment. Browse the filtered results to see which models can accept visual input. Once you've got your filtered list, you can check out the model details or try one in the Playground to test how it handles image-based prompts. 🧪 Test Before You Build Before you plug a model into your agent and start wiring things together, it’s a good idea to test how the model handles image input on its own. This gives you a quick feel for the model’s behavior and helps you catch any limitations before you're deep into building. You can do this in the Playground, where you can upload an image and pair it with a simple prompt like: “Describe the contents of this image.” OR “Summarize what’s happening in this screenshot.” If the model supports image input, you’ll be able to attach a file and get a response based on its visual analysis. If you don’t see the option to upload an image, double-check that the model you’ve selected has image capabilities—this is usually a model issue, not a UI bug. 🔁 Recap Here’s a quick rundown of what we covered: Not all models support image input—you’ll need a multimodal model specifically built to handle visual data. Most platforms won’t let you upload an image unless the model supports it, so if you don’t see that option, it’s probably a model limitation. You can use the AI Toolkit’s Model Catalog to filter models by capability—just check the box for Image Attachment. Test the model in the Playground before integrating it into your agent to make sure it behaves the way you expect. 📺 Want to Go Deeper? Check out my latest video on how to choose the right model for your agent—it’s part of the Build an Agent Series, where I walk through the building blocks of turning an idea into a working AI agent. And if you’re looking to sharpen your model instincts, don’t miss Model Mondays—a weekly series that helps developers like you build your Model IQ, one spotlight at a time. Whether you’re just starting out or already building AI-powered apps, it’s a great way to stay current and confident in your model choices. 👉 Explore the series and catch the next episode: aka.ms/model-mondays/rsvp If you're just getting started with building agents, check out our Agents for Beginners curriculum. And for all your general AI and AI agent questions, join us in the Azure AI Foundry Discord! You can find me hanging out there answering your questions about the AI Toolkit. I'm looking forward to chatting with you there! Whatever you're building, the right model is out there—and with the right tools, you'll know exactly how to find it.
Is AI Foundry in new exam for DP-100
A 25-30% of the DP-100 exam is now dedicated to Optimizing Language Models for AI Applications - is this requiring Azure AI Foundry? It doesn't say specifically in the study guide: https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100 Also, the videos could benefit from being updated to cover the changes as of 16 January 2025.
Monitoring and Evaluating LLMs in Clinical Contexts with Azure AI Foundry
👀 Missed Session 02? Don’t worry—you can still catch up. But first, here’s what AI HLS Ignited is all about: What is AI HLS Ignited? AI HLS Ignited is a Microsoft-led technical series for healthcare innovators, solution architects, and AI engineers. Each session brings to life real-world AI solutions that are reshaping the Healthcare and Life Sciences (HLS) industry. Through live demos, architectural deep dives, and GitHub-hosted code, we equip you with the tools and knowledge to build with confidence. Session 02 Recap: In this session, we introduced MedEvals, an end-to-end evaluation framework for medical AI applications built on Azure AI Foundry. Inspired by Stanford’s MedHELM benchmark, MedEvals enables providers and payers to systematically validate performance, safety, and compliance of AI solutions across clinical decision support, documentation, patient communication, and more. 🧠 Why Scalable Evaluation Is Critical for Medical AI "Large language models (LLMs) hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information." — Evaluating large language models in medical applications: a survey As AI systems become deeply embedded in healthcare workflows, the need for rigorous evaluation frameworks intensifies. Although large language models (LLMs) can augment tasks ranging from clinical documentation to decision support, their deployment in patient-facing settings demands systematic validation to guarantee safety, fidelity, and robustness. Benchmarks such as MedHELM address this requirement by subjecting models to a comprehensive battery of clinically derived tasks built on dataset (ground truth), enabling fine-grained, multi-metric performance assessment across the full spectrum of clinical use cases. However, shipping a medical LLM is only step one. Without a repeatable, metrics-driven evaluation loop, quality erodes, regulatory gaps widen, and patient safety is put at risk. This project accelerates your ability to operationalize trustworthy LLMs by delivering plug-and-play medical benchmarks, configurable evaluators, and CI/CD templates—so every model update triggers an automated, domain-specific “health check” that flags drift, surfaces bias, and validates clinical accuracy before it ever reaches production. 🚀 How to Get Started with MedEvals Kick off your MedEvals journey by following our curated labs. Newcomers to Azure AI Foundry can start with the foundational workflow; seasoned practitioners can dive into advanced evaluation pipelines and CI/CD integration. 🧪 Labs 🧪 Foundry Basics & Custom Evaluations: 🧾 Notebook Authenticate, initialize a Foundry project, run built-in metrics, and build custom evaluators with EvalAI and PromptEval. 🧪 Search & Retrieval Evaluations: 🧾 Notebook Prepare datasets, execute search metrics (precision, recall, NDCG), visualize results, and register evaluators in Foundry. 🧪 Repeatable Evaluations & CI/CD: 🧾 Notebook Define evaluation schemas, build deterministic pipelines with PyTest, and automate drift detection using GitHub Actions. 🏥 Use Cases 📝 Creating Your Clinical Evaluation with RevCycle Determinations Select a model and metric that best supports the determination behind the rationale made on AI-assisted prior authorizations based on real payor policy. This notebook use case includes: Selecting multiple candidate LLMs (e.g., gpt-4o, o1) Breaking down determinations both in deterministic results (approved vs rejected) and the supporting rationale and logic. Running evaluations across multiple dimensions Combining deterministic evaluators and LLM-as-a-Judge methods Evaluating the differential impacts of evaluators on the rationale across scenarios 🧾Get Started with the Notebook Why it matters: Enables data-driven metric selection for clinical workflows, ensures transparent benchmarking, and accelerates safe AI adoption in healthcare. 📝 Evaluating AI Medical Notes Summarization Applications Systematically assess how different foundation models and prompting strategies perform on clinical summarization tasks, following the MedHELM framework. This notebook use case includes: Preparing real-world datasets of clinical notes and summaries Benchmarking summarization quality using relevance, coherence, factuality, and harmfulness metrics Testing prompting techniques (zero-shot, few-shot, chain-of-thought prompting) Evaluating outputs using both automated metrics and human-in-the-loop scoring 🧾Get Started with the Notebook Why it matters: Ensures responsible deployment of AI applications for clinical summarization, guaranteeing high standards of quality, trustworthiness, and usability. 📣 Join Us for the Next Session Help shape the future of healthcare by sharing AI HLS Ignited with your network—and don’t miss what’s coming next! 📅 Register for the upcoming session → AI HLS Ignited Event Page 💻 Explore the code, demos, and architecture → AI HLS Ignited GitHub Repository
