Seoul AI Hub & Microsoft MVPs Empower Citizens with AI Skills — No Coding Required
Seoul AI Hub, an AI-specialized support organization under the Seoul Metropolitan Government, is dedicated to fostering the city’s AI industry ecosystem through talent development, startup incubation, and public education. In partnership with Microsoft MVPs, the hub is making AI accessible to all through the AI Frontiers Series — blending expert talks with hands-on workshops. “AI is no longer just for experts; it’s a tool for everyone,” says Chan-jin Park, Director of Seoul AI Hub. The collaboration between Seoul AI Hub and Microsoft MVPs demonstrates the transformative power of community-led expertise. MVPs such as Jaeseok Lee, Heo seok, Haesun Park, and Minseok Song brought their technical leadership to the forefront — integrating advanced AI concepts with practical skills that citizens could immediately use. From explaining multi-agent architectures to building custom Copilot solutions, their sessions showed how complex AI tools can be democratized for non-developers. Beyond teaching, these MVPs are active contributors to the global AI ecosystem. Minseok Song maintains the Co-op Translator open-source project, integrating AI-based translation workflows into real-world scenarios. Jaeseok Lee leads Korea’s Power Platform User Group, connecting business users and developers to collaborate on Copilot Studio innovations. This kind of community-driven leadership extends the impact of Microsoft technologies far beyond corporate settings. These events also reflect how the MVP community is growing more diverse in expertise and audience reach. Participants came from varied backgrounds — students, entrepreneurs, office workers, and hobbyists — all united by a desire to understand and use AI meaningfully. For many attendees, this was their first encounter with building AI agents, and the supportive environment encouraged experimentation and collaboration. MVPs not only shared technical knowledge but also their own journeys: how they discovered Microsoft AI, grew into community leaders, and applied their skills to solve local and global challenges. Such stories inspire the next generation of community builders and potential MVPs. AI Frontiers Series Summer Sessions Recent events at the Seoul AI Hub where MVP participated included: July 22 featured a deep-dive seminar on “Open AI Technologies for Survival in the AI Frontier Era,” covering multi-agent strategies, LLM and multimodal trends, and real-world open-source AI applications. Aug 12 brought the AI Agent Bootcamp for Non-Developers, where 80 registered citizens learned to create Copilot agents without code. Participants explored integrating AI agents into Microsoft Teams and M365, building document-driven assistants, and deploying multi-channel solutions. “Copilot Studio allows anyone to build their own ChatGPT-like agent. The key is not just creating one agent, but learning how to design multiple agents that work together to solve real problems,” said Jaeseok Lee, Microsoft Copilot Studio MVP. These back-to-back sessions show what’s possible when technical expertise, open-source spirit, and a commitment to public education come together. The impact extends beyond the events themselves — sparking curiosity, building confidence, and equipping citizens to harness AI in ways that are relevant to their lives and work The AI Frontiers Series proves that when experts and communities connect, technology becomes more inclusive and impactful. By lowering the barrier to AI adoption, Seoul AI Hub and Microsoft MVPs are equipping citizens with skills for the future. To explore upcoming sessions or get involved, visit the Seoul AI Hub website and join the movement to make AI a tool for everyone.MVP Collective Launches In-Depth Guide on SharePoint Content AI
MVP and Regional Director Gokan Ozcifci, together with eight fellow Microsoft MVPs, has co-authored SharePoint Content AI, Solutions and Advanced Administration - a new book that delves into the intersection of artificial intelligence and SharePoint. We spoke with the authors to learn more about their collaboration, what inspired the project, and the key themes they explore. What inspired you to collaborate on this eBook, and how did the idea for focusing on AI and SharePoint come about? Gokan Ozcifci, Belgium: The inspiration behind this book stems from observing how rapidly AI is transforming the way organizations manage content, particularly within Microsoft 365 and SharePoint. As this transformation accelerated, I noticed a growing gap: many industry experts, digital transformation directors, and business leaders were eager to embrace these AI-driven tools but found it difficult to understand how capabilities like Autofill, AI-powered metadata, OCR, or governance solutions translate into real-world value. That’s when a group of SharePoint enthusiasts, MVPs, consultants, and practice owners joined forces with a shared goal—to bridge that gap. We set out to create more than just a technical guide. We aimed to build a resource grounded in practical experience, offering clear explanations and actionable insights for those navigating the evolving world of Content AI. SharePoint naturally became our focal point due to its central role in enterprise content management and its rapid evolution in tandem with Microsoft’s AI strategy. Our goal was to demystify the technology, understand the requirements, translate the needs, and show how AI can empower organizations to manage content more intelligently, securely, and efficiently. Can you share a real-world example where AI significantly enhanced SharePoint content management or administration? Frane Borozan – MVP, Croatia: A global enterprise had a use case to classify thousands of legacy contracts in SharePoint. SharePoint content AI extracts metadata, such as expiration dates, the names of signatories, and the validity period of the contract, as well as similar information that is available within the contract. This cut manual effort by almost 90% to hire the workforce to review all these legacy contracts. Noorez Khamis – MVP, Canada: For one client they previously had 5–10 interns manually logging into 20 banking websites each month to download client statements, upload them to SharePoint, tag metadata, and update Salesforce—an inefficient and error-prone process. Now, an automated solution using Power Automate Desktop Flows handles document retrieval, Syntex extracts key metadata, and a validation Power App ensures data accuracy before integrating with Salesforce for approvals and updates. This end-to-end system eliminates manual effort, increases accuracy, and streamlines document processing across platforms. Drew Madelung – MVP, USA: As an M365 consultant, I work with multiple customers and write unique and complex statements of work. I actively utilize SharePoint Content AI features, such as autofill columns, to help summarize and provide key metadata from statements of work, enabling me to discover details from prior and existing projects where overlap is likely to occur. Mike Maadarani – MVP, Canada: AI was deployed at a university to manage their application admissions process. The Content AI significantly improved the classification and extraction of information from various application formats. This process reduced manual work by over 98%, resulting in substantial savings and a high return on investment for the client. Antonio Maio – MVP, Canada: I had a client who greatly benefited from Content AI models in SharePoint Online. They’re a corporate real estate firm that utilizes Content AI models to process lease agreements and rental contracts, automatically extracting key metadata values from these content types. This metadata automatically populates columns in SharePoint libraries, which then drives business process automation and retention policies for those documents. This all happens by users simply uploading new documents into SharePoint libraries. How do you see the role of AI evolving in the SharePoint ecosystem over the next few years? Frane Borozan – MVP, Croatia: SharePoint, as the content management platform, doesn't have a future without the help of AI. Use cases are varied, ranging from extracting metadata from content to helping create new content. I believe that with the help of AI, the possibilities of SharePoint are unlimited. Noorez Khamis – MVP, Canada: Copilot is making SharePoint the go-to content management system by transforming how you discover, create, and interact with content. You can now ask for what you need, generate pages from your existing documents, and get personalized answers through AI-powered SharePoint Agents. With more intelligent automation, beautiful intranet design, and fewer clicks, SharePoint feels more like an intelligent assistant than a static site. Vlad Catrinescu – MVP, Canada: AI will continue transforming how we work, and SharePoint is no exception. Today, we’re already seeing AI help fill in metadata for document libraries. But imagine if AI could go further: automatically suggest and create the right columns, build content types based on the documents you upload, or even configure web parts through natural language prompts. SharePoint has always been a powerful platform, but it hasn’t always been the easiest to use. AI has the potential to make that power more accessible to every user, not just the experts. Drew Madelung – MVP, USA: I would like to see SharePoint Content AI evolve in a way that identifies opportunities for AI using existing content that automatically configures it without user intervention to improve discoverability. The ability to configure and work with these AI features should have a minimal learning curve and be integrated seamlessly, without requiring specialized technical skills. Mike Maadarani – MVP, Canada: As artificial intelligence continues to advance, it is anticipated that content management will become fully automated, reducing the need for administrators to establish and enforce rules. AI algorithms will be significantly more sophisticated, enhancing their ability to comprehend an organization's policies, the nature of the content being added, and the necessary actions required by the rules. Antonio Maio – MVP, Canada: I think auto-fill columns will have a significant impact on SharePoint Online. Metadata is a core element of good information management, but we know that users don’t want to fill in metadata. They’re busy and often move too quickly from task to task, leaving little time to provide a wealth of metadata elements. SharePoint’s auto-fill columns offer an easy way for us to automatically extract metadata, based on a prompt that’s supplied to the column. Joanne Klein – MVP, Canada: As a security and compliance professional, I observe the evolution of AI data governance, which aims to control the proliferation, access, and lifecycle of AI solutions across SharePoint within the enterprise. We need to elevate AI to be a core pillar within an organization's holistic data governance strategy. What advice would you give to SharePoint professionals who are just beginning to explore AI-powered capabilities? Frane Borozan – MVP, Croatia: If you're starting with AI in SharePoint, taxonomy tagging is a perfect first step. It shows how AI can reduce manual effort and bring structure to your content. Set up a managed metadata column linked to your term store and let AI handle tagging based on document content. It’s a simple way to improve search, consistency, and governance—without heavy customization. Start here, learn the basics, and expand as you go. Noorez Khamis – MVP, Canada: Start with the basics and learn how to prompt effectively while using the full Microsoft 365 Copilot capabilities across all the workplace tools you use every day, such as Teams, SharePoint, Word, Excel, PowerPoint, and Outlook. Take the time to revise your prompts and use templates that have been proven to work, saving you time, streamlining tasks, and boosting productivity. In SharePoint specifically, explore how to create pages, rewrite content, and use AI-powered SharePoint agents. Vlad Catrinescu – MVP, Canada: Get hands-on as early as possible. Theory is great, but real understanding comes from testing in your environment. Set up a lab, start small, and explore practical use cases where AI can help automate or enhance existing processes. With the Pay-As-You-Go model, there’s no upfront cost—you only pay for what you use. That said, I highly recommend setting a budget cap in Azure to avoid surprises. Drew Madelung – MVP, USA: SharePoint remains essentially unchanged in many ways, and it is still essential to understand the concepts of content types, columns, and permission hierarchies to implement advanced AI solutions against your organization's content effectively. Mike Maadarani – MVP, Canada: IT professionals must stay current with evolving technologies, particularly in the rapidly changing field of AI. With the emergence of AI agents, I recommend acquiring skills in creating, managing, and deploying agents within Microsoft 365 to enhance the integration and utilization of AI in their organizations. Antonio Maio – MVP, Canada: Be curious about AI - play with the AI technology that’s built into SharePoint; experiment with it to see what best benefits your organization to improve how you specifically manage information. Your experience will be different than everyone else’s, so try different things to figure out what works for you and your users. Joanne Klein – MVP, Canada: If your SharePoint setup is a mess, your AI will be too. Solid, well-defined structure and smart governance (site owner stewardship, explicit permissions, retention/deletion to clean up ROT, and data protection controls) are like laying concrete before building—skip it, and your AI’s standing on quicksand. Access the full 194-page e-book, SharePoint Content AI, Solutions and Advanced Administration, at the following link SharePoint Content AI, Solutions and Advanced AdministrationGenAI for Scam & Fraud Detection
Chief Technology Officer and Microsoft Regional Director, Dr David Goad, recently highlighted the transformative potential of generative AI in combating financial scams, in a recent live stream on the Microsoft Reactor YouTube channel. With a wealth of experience in artificial intelligence and machine learning, David shared his expertise and insights, providing practical examples on the applications of generative AI and Azure AI Foundry for scam and fraud detection in the banking sector. The rise of digital banking and fraud David Goad began by setting the scene, discussing the significant shift towards digital banking over the past decade. This transition has brought numerous benefits, including convenience and access to information, but it has also led to a surge in financial scams and fraud. The banking industry, particularly in Australia, has seen billions of dollars lost annually due to these fraudulent activities. Key industry trends David highlighted several key trends and challenges in managing fraud and scams within the banking sector. He pointed out that phishing remains one of the most prevalent methods used by fraudsters, with targeted spear phishing and whale phishing aimed at senior individuals. This demographic is often targeted, leading to significant financial losses and stress. Additionally, phone calls, text messages, and emails are common delivery methods for scams. Opportunities for improvement with generative AI David Goad emphasized the opportunities for improvement in the scam detection process through the use of generative AI. He explained that generative AI can enhance various aspects of fraud detection, including identifying fraudulent emails and texts, summarizing customer complaints, categorizing complaints for efficient routing, and evaluating customer sentiment. By leveraging generative AI, banks can improve the accuracy and efficiency of their fraud detection processes, ultimately reducing customer frustration and enhancing service levels. Demonstrating generative AI for phishing detection To illustrate the practical application of generative AI, David demonstrated Azure AI Foundry and Azure OpenAI Studio. He showcased how generative AI can be trained to identify phishing emails by fine-tuning a model with a dataset of classified emails. The model that David presented, was able to classify emails as phishing or non-phishing and provide explanations for its decisions, demonstrating the potential for generative AI to streamline the fraud detection process. Learn more David Goad's presentation emphasized the transformative potential of generative AI in the banking sector and the opportunities that adopting generative AI can bring to help customer experiences. For those interested in learning more about this topic, David has written a LinkedIn article that delves deeper into the use of generative AI in fraud detection. To watch the full recording of David Goad's insightful presentation and technical demonstrations, visit the Microsoft Reactor YouTube channel.Building Custom Chat AI: A Comprehensive Guide for Developers
In today's rapidly evolving digital landscape, the integration of artificial intelligence (AI) into business operations has become a pivotal strategy for companies aiming to enhance their customer engagement and streamline their processes. This article delves into the foundational steps and considerations for developers embarking on the journey of building a custom chat AI for their company website. From understanding the core concepts of AI to selecting the right models and implementing effective prompt engineering techniques, this guide provides a comprehensive overview to help developers navigate the complexities of AI development. Whether you are a beginner or have some experience in the field, the insights shared here will equip you with the knowledge and tools needed to create a robust and efficient chat AI tailored to your business needs. A discussion will be held with https://www.linkedin.com/in/nityan/, Senior Cloud Advocate at Microsoft specializing in AI, and https://mvp.microsoft.com/en-US/MVP/profile/fe4dbe00-cdb0-ec11-983f-000d3a1017e3, a Chinese AI MVP, to delve into these critical topics. What are the first steps a developer should take when starting to build a custom chat AI for their company website? Nitya: If you are new to AI, start by familiarizing yourself with the core concepts and usage of AI models. A course like https://aka.ms/genai-beginnerscan be a great starting point. Next, get hands-on experience with models by trying out GitHub Models, which are free to use with just a GitHub account. This will help you build your intuition for model selection and prompt engineering. If you already have some experience, the initial steps to building a custom chat AI are as follows: Identify the use case and requirements (e.g., typical questions asked and valid responses). Choose a model to start prototyping (test the question with various models and compare results). If your chat AI is grounded in your data, identify the data sources and formats (where and what). Select an AI app template to jumpstart development and customize it with your model and data choices. How does understanding model choice impact the development of a custom chat AI? Nitya: Understanding model choice is crucial for developing a custom chat AI. It involves evaluating models based on three key factors: cost, customization, and performance. Customization: Start by identifying the task you want to execute (e.g., chat, image, embeddings, agents). Filter models that support this capability and validate them with a test prompt to ensure they fit your requirements. This process will narrow down your options from thousands to a few suitable models. Cost: Consider whether the model supports serverless deployments (pay-as-you-go, per token) or managed deployments (subscription-based, per VM). Evaluate costs not just for usage (chat completion) but also for end-to-end development (evaluations, iterative ideation). Performance: Assess models based on latency (e.g., chat completions vs. reasoning models) and the quality and safety of responses. Understand default model characteristics (model card) and perform custom evaluations to ensure quality for your desired prompts dataset. Can you explain the concept of prompt engineering and how it can be applied using GitHub models? Nitya: Prompt engineering involves guiding the model on how to process questions and generate responses to improve quality. Think of developers as teachers and models as students being taught to answer exam questions. Prompt engineering provides a rubric to guide models in giving relevant answers. This includes providing examples, creating personas (e.g., "answer politely using formal language"), defining output formats (e.g., "answer in 1-2 sentences", "reply with results in JSON format"), and configuring model parameters (e.g., temperature, stop-words, top-p, max tokens). When working with GitHub models, you can configure models using the https://github.com/marketplace/models or move to an IDE with the https://learn.microsoft.com/en-us/rest/api/aifoundry/modelinference/API, offering both low-code and code-first options for prompt engineering. What is retrieval augmented generation (RAG), and how does it enhance the ability to chat with data? Wei: https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview?tabs=docs involves grounding user questions in retrieved knowledge from private data sources to ensure responses are relevant to the application scenario. It works by wrapping the initial user prompt in a prompt template to create the final model prompt sent to the model. Thehttps://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview?tabs=docs workflow includes retrieval of knowledge, augmentation of the prompt, and generation of the response. This dynamic process provides relevant grounding data and instructions to contextualize user questions for app-required responses. What are some practical tips for developers to streamline their end-to-end journey from catalog to cloud? Nitya: Here are three tips to get started: Model Selection: Use GitHub Models with diverse test prompts to build intuition for prompt engineering and model capabilities. Compare models side-by-side. Copilot Development: Start with an https://aka.ms/ai-apps. Deploy it to understand the application and its architecture before customizing it to your needs. Validate your development environment and get familiar with tools. Safety & Evaluation: Explore built-in content safety filters and evaluators in the Azure AI platform to understand metrics and effectiveness of your prompt engineering or RAG strategy. Use tracing and App Insights to monitor performance and cost. What are some common challenges developers might face when building a custom chat AI, and how can they overcome them? Nitya and Wei: There are many challenges we can think of - here are three that are important: App Architecture: Understand the app architecture for your scenario (e.g., RAG, multi-agent). Explore existing AI app templates to build intuition and customize one that fits your requirements. Model Choice: Choose models based on cost, quota availability, and flexibility for future configuration. Use the Azure AI model inference API to abstract provider-specific SDKs and decouple your code from your choice, allowing for easier model swaps later. Observability: Debug issues in app development or execution performance. Use platforms and tools that bring observability to the end-to-end workflow. Activate App Insights and use tracing tools to generate telemetry for insights locally or in production. What resources and samples are available for further exploration into this subject? Wei: Explore https://aka.ms/ai-apps, https://aka.ms/genai-beginners, https://aka.ms/rag/azure-ai-foundry, https://aka.ms/aitour/reposand https://microsoft.github.io/generative-ai-for-beginners For more workshops and talks, visit https://aka.ms/aitour/repos. Feel free to check out opensource projects like https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fmicrosoft%2Fautogen&data=05%7C02%7CRochelle.Sonnenberg%40microsoft.com%7C564dabbda6454d889d8308dd867510be%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638814559548370151%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=ihWSr5jedQIQKe6F%2Bk0Cwm5edG6Xc62jvhSQ9I0w6S0%3D&reserved=0 https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdocs.llamaindex.ai%2Fen%2Fstable%2F&data=05%7C02%7CRochelle.Sonnenberg%40microsoft.com%7C564dabbda6454d889d8308dd867510be%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638814559548398018%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=04lUnuTY9g13jf3HvtYgjrRcDycipl2Qq%2BQ55kXVBZI%3D&reserved=0, https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpython.langchain.com%2Fdocs%2Fintroduction%2F&data=05%7C02%7CRochelle.Sonnenberg%40microsoft.com%7C564dabbda6454d889d8308dd867510be%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638814559548436587%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=4Wt%2FQavECvoZ%2BVfgn0qhflrCHKWVRmkzpxxfNL4jQQU%3D&reserved=0, and https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fcamel-ai%2Fcamel&data=05%7C02%7CRochelle.Sonnenberg%40microsoft.com%7C564dabbda6454d889d8308dd867510be%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638814559548424270%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=8MwQd1vUnaADU3Nkl2mApD1%2FpgSzfWp7McLoyC5WooQ%3D&reserved=0 documentation. As we conclude this exploration into building a custom chat AI for your company website, it's clear that the journey is both challenging and rewarding. By understanding the core concepts of AI, selecting the right models, and mastering prompt engineering, developers can create a powerful tool that enhances customer engagement and streamlines business operations. The insights and practical tips shared in this article provide a solid foundation for embarking on this journey. Remember, the key to success lies in continuous learning and adaptation. As AI technology evolves, you should also adapt your approach to developing and refining your chat AI. Stay curious, stay innovative, and most importantly, stay committed to delivering the best possible experience for your users.
