Kickstart Your AI Development with the Model Context Protocol (MCP) Course
Model Context Protocol is an open standard that acts as a universal connector between AI models and the outside world. Think of MCP as “the USB-C of the AI world,” allowing AI systems to plug into APIs, databases, files, and other tools seamlessly. By adopting MCP, developers can create smarter, more useful AI applications that access up-to-date information and perform actions like a human developer would. To help developers learn this game-changing technology, Microsoft has created the “MCP for Beginners” course a free, open-source curriculum that guides you from the basics of MCP to building real-world AI integrations. Below, we’ll explore what MCP is, who this course is for, and how it empowers both beginners and intermediate developers to get started with MCP. What is MCP and Why Should Developers Care? Model Context Protocol (MCP) is a innovative framework designed to standardize interactions between AI models and client applications. In simpler terms, MCP is a communication bridge that lets your AI agent fetch live context from external sources (like APIs, documents, databases, or web services) and even take actions using tools. This means your AI apps are no longer limited to pre-trained knowledge they can dynamically retrieve data or execute commands, enabling far more powerful and context-aware behavior. Some key reasons MCP matters for developers: Seamless Integration of Tools & Data: MCP provides a unified way to connect AI to various data sources and tools, eliminating the need for ad-hoc, fragile integrations. Your AI agent can, for example, query a database or call a web API during a conversation all through a standardized protocol. Stay Up-to-Date: Because AI models can use MCP to access external information, they overcome the training data cutoff problem. They can fetch the latest facts, figures, or documents on demand, ensuring more accurate and timely responses. Industry Momentum: MCP is quickly gaining traction. Originally introduced by Microsoft and Anthropic in late 2024, it has since been adopted by major AI platforms (Replit, Sourcegraph, Hugging Face, and more) and spawned thousands of open-source connectors by early 2025. It’s an emerging standard – learning it now puts developers at the forefront of AI innovation. In short, MCP is transformative for AI development, and being proficient in it will help you build smarter AI solutions that can interact with the real world. The MCP for Beginners course is designed to make mastering this protocol accessible, with a structured learning path and hands-on examples. Introducing the MCP for Beginners Course “Model Context Protocol for Beginners” is an open-source, self-paced curriculum created by Microsoft to teach the concepts and fundamentals of MCP. Whether you’re completely new to MCP or have some experience, this course offers a comprehensive guide from the ground up. Key Features and Highlights: Structured Learning Path: The curriculum is organized as a multi-part guide (9 modules in total) that gradually builds your knowledge. It starts with the basics of MCP – What is MCP? Why does standardization matter? What are the use cases? – and then moves through core concepts, security considerations, getting started with coding, all the way to advanced topics and real-world case studies. This progression ensures you understand the “why” and “how” of MCP before tackling complex scenarios. Hands-On Coding Examples: This isn’t just theory – practical coding examples are a cornerstone of the course. You’ll find live code samples and mini-projects in multiple languages (C#, Java, JavaScript/TypeScript, and Python) for each concept. For instance, you’ll build a simple MCP-powered Calculator application as a project, exploring how to implement MCP clients and servers in your preferred language. By coding along, you cement your understanding and see MCP in action. Real-World Use Cases: The curriculum illustrates how MCP applies to real scenarios. It discusses practical use cases of MCP in AI pipelines (e.g. an AI agent pulling in documentation or database info on the fly) and includes case studies of early adopters. These examples help you connect what you learn to actual applications and solutions you might develop in your job. Broad Language Support: A unique aspect of this course is its multi-language approach – both in terms of programming and human languages. The content provides code implementations in several popular programming languages (so you can learn MCP in the context of C#, Java, Python, JavaScript, or TypeScript, as you prefer). In addition, the learning materials themselves are available in multiple human languages (English, plus translations like French, Spanish, German, Chinese, Japanese, Korean, Polish, etc.) to support learners worldwide. This inclusivity ensures that more developers can comfortably engage with the material. Up-to-Date and Open-Source: Being hosted on GitHub under MIT License, the curriculum is completely free to use and open for contributions. It’s maintained with the latest updates for example, automated workflows keep translations in sync so all language versions stay current. As MCP evolves, the course content can evolve with it. You can even join the community to suggest improvements or add content, making this a living learning resource. Official Resources & Community Support: The course links to official MCP documentation and specs for deeper reference, and it encourages learners to join thehttps;//aka.ms/ai/discord to discuss and get help. You won’t be learning alone; you can network with experts and peers, ask questions, and share progress. Microsoft’s open-source approach means you’re part of a community of practitioners from day one. Course Outline: (Modules at a Glance) Introduction to MCP: Overview of MCP, why standardization matters in AI, and the key benefits and use cases of using MCP. (Start here to understand the big picture.) Core Concepts: Deep dive into MCP’s architecture – understanding the client-server model, how requests and responses work, and the message schema. Learn the fundamental components that make up the protocol. Security in MCP: Identify potential security threats when building MCP-based systems and learn best practices to secure your AI integrations. Important for anyone planning to deploy MCP in production environments. Getting Started (Hands-On): Set up your environment and create your first MCP server and client. This module walks through basic implementation steps and shows how to integrate MCP with existing applications, so you get a service up and running that an AI agent can communicate with. MCP Calculator Project: A guided project where you build a simple MCP-powered application (a calculator) in the language of your choice. This hands-on exercise reinforces the concepts by implementing a real tool – you’ll see how an AI agent can use MCP to perform calculations via an external tool. Practical Implementation: Tips and techniques for using MCP SDKs across different languages. Covers debugging, testing, validation of MCP integrations, and how to design effective prompt workflows that leverage MCP’s capabilities. Advanced Topics: Going beyond the basics – explore multi-modal AI workflows (using MCP to handle not just text but other data types), scalability and performance tuning for MCP servers, and how MCP fits into larger enterprise architectures. This is where intermediate users can really deepen their expertise. Community Contributions: Learn how to contribute to the MCP ecosystem and the curriculum itself. This section shows you how to collaborate via GitHub, follow the project’s guidelines, and even extend the protocol with your own ideas. It underlines that MCP is a growing, community-driven standard. Insights from Early Adoption: Hear lessons learned from real-world MCP implementations. What challenges did early adopters face? What patterns and solutions worked best? Understanding these will prepare you to avoid pitfalls in your own projects. Best Practices and Case Studies: A roundup of do’s and don’ts when using MCP. This includes performance optimization techniques, designing fault-tolerant systems, and testing strategies. Plus, detailed case studies that walk through actual MCP solution architectures with diagrams and integration tips bringing everything you learned together in concrete examples. Who Should Take This Course? The MCP for Beginners course is geared towards developers if you build or work on AI-driven applications, this course is for you. The content specifically welcomes: Beginners in AI Integration: You might be a developer who's comfortable with languages like Python, C#, or Java but new to AI/LLMs or to MCP itself. This course will take you from zero knowledge of MCP to a level where you can build and deploy your own MCP-enabled services. You do not need prior experience with MCP or machine learning pipelines the introduction module will bring you up to speed on key concepts. (Basic programming skills and understanding of client-server or API concepts are the only prerequisites.) Intermediate Developers & AI Practitioners: If you have some experience building bots or AI features and want to enhance them with real-time data access, you’ll benefit greatly. The course’s later modules on advanced topics, security, and best practices are especially valuable for those looking to integrate MCP into existing projects or optimize their approach. Even if you've dabbled in MCP or a similar concept before, this curriculum will fill gaps in knowledge and provide structured insights that are hard to get from scattered documentation. AI Enthusiasts & Architects: Perhaps you’re an AI architect or tech lead exploring new frameworks for intelligent agents. This course serves as a comprehensive resource to evaluate MCP for your architecture. By walking through it, you’ll understand how MCP can fit into enterprise systems, what benefits it brings, and how to implement it in a maintainable way. It’s perfect for getting a broad yet detailed view of MCP’s capabilities before adopting it within a team. In essence, anyone interested in making AI applications more connected and powerful will find value here. From a solo hackathon coder to a professional solution architect, the material scales to your need. The course starts with fundamentals in an easy-to-grasp manner and then deepens into complex topics appealing to a wide range of skill levels. Prerequisites: The official prerequisites for the course are minimal: you should have basic knowledge of at least one programming language (C#, Java, or Python is recommended) and a general understanding of how client-server applications or APIs work. Familiarity with machine learning concepts is optional but can help. In short, if you can write simple programs and understand making API calls, you have everything you need to start learning MCP. Conclusion: Empower Your AI Projects with MCP The Model Context Protocol for Beginners course is more than just a tutorial – it’s a comprehensive journey that empowers you to build the next generation of AI applications. By demystifying MCP and equipping you with hands-on experience, this curriculum turns a seemingly complex concept into practical skills you can apply immediately. With MCP, you unlock capabilities like giving your AI agents real-time information access and the ability to use tools autonomously. That means as a developer, you can create solutions that are significantly more intelligent and useful. A chatbot that can search documents, a coding assistant that can consult APIs or run code, an AI service that seamlessly integrates with your database – all these become achievable when you know MCP. And thanks to this beginners-friendly course, you’ll be able to implement such features with confidence. Whether you are starting out in the AI development world or looking to sharpen your cutting-edge skills, the MCP for Beginners course has something for you. It condenses best practices, real-world lessons, and robust techniques into an accessible format. Learning MCP now will put you ahead of the curve, as this protocol rapidly becomes a cornerstone of AI integrations across the industry. So, are you ready to level up your AI development skills? Dive into the https://aka.ms/mcp-for-beginnerscourse and start building AI agents that can truly interact with the world around them. With the knowledge and experience gained, you’ll be prepared to create smarter, context-aware applications and be a part of the community driving AI innovation forward.AI Sparks: Unleashing Agents with the AI Toolkit
The final episode of our "AI Sparks" series delved deep into the exciting world of AI Agents and their practical implementation. We also covered a fair part of MCP with Microsoft AI Toolkit extension for VS Code. We kicked off by charting the evolutionary path of intelligent conversational systems. Starting with the rudimentary rule-based Basic Chatbots, we then explored the advancements brought by Basic Generative AI Chatbots, which offered contextually aware interactions. Then we explored the Retrieval-Augmented Generation (RAG), highlighting its ability to ground generative models in specific knowledge bases, significantly enhancing accuracy and relevance. The limitations were also discussed for the above mentioned techniques. The session was then centralized to the theme – Agents and Agentic Frameworks. We uncovered the fundamental shift from basic chatbots to autonomous agents capable of planning, decision-making, and executing tasks. We moved forward with detailed discussion on the distinction between Single Agentic systems, where one core agent orchestrates the process, and Multi-Agent Architectures, where multiple specialized agents collaborate to achieve complex goals. A key part of building robust and reliable AI Agents, as we discussed, revolves around carefully considering four critical factors. Firstly, Knowledge-Providing agents with the right context is paramount for them to operate effectively and make informed decisions. Secondly, equipping agents with the necessary Actions by granting them access to the appropriate tools allows them to execute tasks and achieve desired outcomes. Thirdly, Security is non-negotiable; ensuring agents have access only to the data and services they genuinely need is crucial for maintaining privacy and preventing unintended actions. Finally, establishing robust Evaluations mechanisms is essential to verify that agents are completing tasks correctly and meeting the required standards. These four pillars – Knowledge, Actions, Security, and Evaluation – form the bedrock of any successful agentic implementation. To illustrate the transformative power of AI Agents, we explored several interesting use cases and applications. These ranged from intelligent personal assistants capable of managing schedules and automating workflows to sophisticated problem-solving systems in domains like customer service. A significant portion of the session was dedicated to practical implementation through demonstrations. We highlighted key frameworks that are empowering developers to build agentic systems.: Semantic Kernel: We highlighted its modularity and rich set of features for integrating various AI services and tools. Autogen Studio: The focus here was on its capabilities for facilitating the creation and management of multi-agent conversations and workflows. Agent Service: We discussed its role in providing a more streamlined and managed environment for deploying and scaling AI agents. The major point of attraction was that these were demonstrated using the local LLMs which were hosted using AI Toolkit. This showcased the ease with which developers can utilize VS Code AI toolkit to build and experiment with agentic workflows directly within their familiar development environment. Finally, we demystified the concept of Model Context Protocol (MCP) and demonstrated how seamlessly it can be implemented using the Agent Builder within the VS Code AI Toolkit. We demonstrated this with a basic Website development using MCP. This practical demonstration underscored the toolkit's power in simplifying the development of complex solutions that can maintain context and engage in more natural, multi-step interactions. The "AI Sparks" series concluded with a discussion, where attendees had a clearer understanding of the evolution, potential and practicalities of AI Agents. The session underscored that we are on the cusp of a new era of intelligent systems that are not just reactive but actively work alongside us to achieve goals. The tools and frameworks are maturing, and the possibilities for agentic applications are sparking innovation across various industries. It was an exciting journey, and engagement during the final session on AI Sparks around Agents truly highlighted the transformative potential of this field. "AI Sparks" Series Roadmap: The "AI Sparks" series delved deeper into specific topics using AI Toolkit for Visual Studio Code, including: Introduction to AI toolkit and feature walkthrough: Introduction to the AI Toolkit extension for VS Code a powerful way to explore and integrate the latest AI models from OpenAI, Meta, Deepseek, Mistral, and more. Introduction to SLMs and local model with use cases: Explore Small Language Models (SLMs) and how they compare to larger models. Building RAG Applications: Create powerful applications that combine the strengths of LLMs with external knowledge sources. Multimodal Support and Image Analysis: Working with vision models and building multimodal applications. Evaluation and Model Selection: Evaluate model performance and choose the best model for your needs. Agents and Agentic Frameworks: Exploring the cutting edge of AI agents and how they can be used to build more complex and autonomous systems. The full playlist of the series with all the episodes of "AI Sparks" is available at AI Sparks Playlist. Continue the discussion and questions in Microsoft AI Discord Community where we have a dedicated AI-sparks channel. All the code samples can be found on AI_Toolkit_Samples .We look forward to continuing these insightful discussions in future series!Build an AI Powered Image App – Microsoft Learn Challenge
A new Microsoft Learn challenge module just dropped and it’s a perfect bite-sized project to get a taste for the latest AI technology and how you can start using it in fun and fast ways. In this module, Challenge project - Add image analysis and generation capabilities to your application, you will combine different AI image technology while deploying it to a web app, resulting in a great demo project to show off your skills in an image analysing and image generating app.
Introducing Model Mondays - Build Your AI Model IQ With This Weekly Hands-on Series
Have you felt overwhelmed by the pace of AI innovation? How do you keep up with the latest model news? How do you pick the right model from 1800+ options? How can you learn about best practices from others and get hands-on experience? This is where Model Mondays comes in. Join us starting March 10 for the 8-part season kickoff - read the blog post to learn more.Prompt Engineering Simplified: AI Toolkit's Prompt Builder
In the age of generative AI, crafting effective prompts is no longer a nice-to-have, it's a must-have. Understanding how to communicate with these underlying models is the key to unlocking their true potential and getting the results we need. What are Prompts? Every time we want to communicate to the language model, we give set of instructions to these models, we refer to these inputs as Prompts. Prompts play a very crucial role while working with the GenAI models. The quality of a prompt directly impacts the output of GenAI models. Precise and well-crafted prompts are crucial for achieving desired results. What factors crafts an optimal Prompt? Crafting an optimal requires balancing clarity, specificity and context. Besides these, constraints are a critical factor in crafting effective prompts. Specificity Clearly define the expectations. The prompt should leave no room for misinterpretation. Precise language is the key. Avoid vague language. Vague prompts lead to vague or irrelevant responses. e.g., “Tell me about history” ➔ “Explain the economic causes of the French Revolution”. Clarity Use simple, unambiguous language. Avoid jargon unless your audience expects it, recommended to use action verbs like "write," "summarize," "explain," "translate". Context Provide background for e.g., “As a beginner in coding, how do I write a Python loop?”. Give the LLM enough context to understand the situation. Include relevant details, keywords, and background information Conciseness Trim unnecessary words (e.g., “Describe photosynthesis” vs. “Can you tell me about how plants use sunlight?”). Ensure the prompt remains relevant to the desired output Tone & Audience Alignment Match the tone to the goal (formal, casual, instructive). Example: For kids, “Explain how rainbows form in simple terms.” Explicit Instructions Directly state what is needed e.g., “Compare X and Y”, “List pros and cons,” “Write a poem about…”. Guiding Constraints Limit scope to avoid overly broad answers e.g., “Focus on environmental impacts, not economic ones”. Constraints reduce ambiguity, focus responses, and improve relevance. Few example constraints, Format: “Summarize in 3 bullet points.” Length: “Explain in 2 sentences.” Scope: “Focus on environmental impacts, not economic ones.” Style/Tone: “Write a casual email,” or “Use non-technical terms.” Technical limits: “Keep code examples under 50 lines.” Few advanced Considerations for AI/LLM Prompts Examples or Demonstrations Include examples to set expectations for e.g., “Write a limerick like this: There once was a cat from Peru…”. Step-by-Step Guidance Break complex tasks into steps for e.g., “First analyze the Python code, then suggest solutions”. Role Assignment Assign roles to guide the AI for e.g., “Act as a historian explaining World War 2”. Avoid Bias Neutral phrasing ensures fair responses for e.g., “Discuss pros and cons of renewable energy” vs. “Why is solar energy bad?” the former is a well-formed prompt. Prompt engineering is an iterative process. Experiment with different phrasings and structures to see what works best. Analyze the LLM's responses and refine prompts accordingly. Make adjustments to improve the accuracy and relevance of the output. Prompt Builder: From the above section, we know that crafting effective prompts is essential for robust AI engagement. Prompt Builder tool on AI Toolkit helps in this enhancement by streamlining the whole process of crafting prompts. Prompt builder helps the users by helping in the following areas, o Prompt Creation, Modification, and Evaluation: Customize prompts through an accessible and straightforward interface. o AI-Assisted Prompt Generation: Articulate the project concept using everyday language, and the AI-powered feature will produce prompts for your exploration. o Organized Output Capability: Craft the prompts to yield outputs in a consistent, standardized and predictable manner. o Automated Code Generation for Prompt Usage: Following model and prompt experimentation, transition to coding immediately by accessing automatically generated, executable Python code. This tool has three sections on the UI. Prompt configuration Response History Prompt Configuration Section: In the Prompt configuration section, there are 4 major sub sections, Model System Prompt User Prompt Add Prompt Model: The Model section is the first subsection of the Prompt Configuration. Here, we select the model to use. The AI Toolkit offers a wide range of models, including remote models served from GitHub and those from providers such as OpenAI, Google, Anthropic, and Nvidia. For this tutorial we will be using OpenAI GPT-4o mini via GitHub System Prompt: In System prompt section, we provide instructions with relevant context to guide the system response. We can think of a system prompt as the "role" we give an AI before we ask it anything, like telling an actor what character to play. Generate Prompt: Upon choosing cloud-based / GitHub / Remote models, a new tool called as “Generate Prompt” is enabled, this is an AI Powered tool especially useful for crafting AI Powered well defined prompts which can be used in the “System Prompt” Section. Upon clicking on the “Generate Prompt” we can see a small window that pops up and asks for the input prompt. This can generate a prompt template by sharing basic details about the task. In this tutorial, let’s ask the LLM to generate prompt about “Professor in university teaching math”. Once the message is updated click on “Generate” button, and in a few seconds, we will have a well-structured prompt in the “System Prompt” section. The prompt that we generated is as follows Provide a detailed syllabus for a university-level mathematics course, including course objectives, weekly topics, assessment methods, and required materials. The syllabus should cover all essential components such as the course title, description, prerequisites, learning outcomes, weekly schedules, and any relevant policies regarding attendance, grading, and participation. # Steps 1. **Course Title and Description**: Clearly state the title of the course and provide a brief description of what the course will cover. 2. **Prerequisites**: List any required courses or knowledge necessary for students to enroll. 3. **Learning Outcomes**: Define what students are expected to learn by the end of the course. 4. **Weekly Schedule**: Outline topics for each week, along with any associated readings or assignments. 5. **Assessment Methods**: Describe how students will be evaluated (e.g., exams, quizzes, projects). 6. **Required Materials**: Include information on textbooks and other resources needed for the course. 7. **Course Policies**: State attendance, grading, and participation rules. # Output Format The output should be formatted as a structured syllabus, presented in clear sections with headings for each part. The document should be detailed yet concise, ideally around 3-5 pages in length. # Examples **Example 1** **Input:** Create a syllabus for a Calculus I course. **Output:** - **Course Title**: Calculus I - **Description**: An introduction to limits, derivatives, and integrals. - **Prerequisites**: Pre-Calculus or equivalent. - **Learning Outcomes**: Students will be able to calculate limits, differentiate basic functions, and understand the Fundamental Theorem of Calculus. - **Weekly Schedule**: - Week 1: Introduction to Limits - Week 2: Continuity - Week 3: Derivatives - ... - **Assessment Methods**: Midterm exam (30%), Final exam (40%), Weekly quizzes (20%), Participation (10%). - **Required Materials**: "Calculus: Early Transcendentals" by James Stewart. - **Course Policies**: Attendance required, late assignments will incur a penalty. **Example 2** **Input:** Design a syllabus for a Linear Algebra course. **Output:** - **Course Title**: Linear Algebra - **Description**: Study vector spaces, matrices, and linear transformations. - **Prerequisites**: None. - **Learning Outcomes**: Mastery of matrix operations and ability to solve systems of linear equations. - **Weekly Schedule**: - Week 1: Introduction to Vector Spaces - Week 2: Matrix Operations - Week 3: Determinants - ... - **Assessment Methods**: Two midterms (50%), Homework assignments (30%), Attendance (20%). - **Required Materials**: "Linear Algebra Done Right" by Sheldon Axler. - **Course Policies**: Participation in class discussions is mandatory. # Notes Ensure that the syllabus is comprehensive and tailored to the specific course topic. Consider including any unique teaching methods or technologies that will be employed during the course. User Prompt: User prompt is the specific question, instruction, or request that a person provides to the AI to elicit a response. It's the direct input from the user that initiates the AI's processing and generation of text. In AI Toolkit for a few models that support the multimodal feature, we can also upload images in this section. For this tutorial let’s input “Explain to me the Fourier equation in simple terms” Add Prompt: If any additional prompt needs to be added, we can configure more User or assistant prompt. So, in a conversation, we have: User Prompt: What the human says. Assistant Prompt: What the AI says. The major configuration part is now completed through this window, its now time to test the responses based on the LLM’s knowledge, in this case how well does GPT 4o mini behave in the role as university-level mathematics professor. In order to test it, we navigate to the next window, the Response section. Response Section: The Response section is where we finally get to see the responses. This section has the “Run” and “View Code” buttons. We can also choose the type of response we need. It can be a simple text or json schema. Upon choosing Json Schema, user will be prompted to “Prepare Schema”. Users can define their own schema or select from example. There are a few examples for the user to choose from. For this tutorial we will be using the simple text format. As we have our setup ready, we can directly click on the “Run” button, In a few seconds we have our well formatted and accurate answer on the screen, AI Toolkit‘s markdown capability can neatly format all the mathematical signs and equations. We can also add this to the “Assistant Prompt” by using the button provided. It provides better example for the LLM in the code later. The result from the LLM now seems very satisfactory with our well-crafted prompt. We can now proceed with the Code generation feature of the Prompt Builder tool of AI Toolkit. Upon clicking the “View Code” button, user is prompted to choose the SDK of their choice. This SDK lets us communicate with the API from the code. For this tutorial, we will use Azure AI Inference SDK. For more details on this SDK refer here. The code requires azure-ai-inference. Install the library by pip install azure-ai-inference """Run this model in Python > pip install azure-ai-inference """ import os from azure.ai.inference import ChatCompletionsClient from azure.ai.inference.models import AssistantMessage, SystemMessage, UserMessage from azure.ai.inference.models import ImageContentItem, ImageUrl, TextContentItem from azure.core.credentials import AzureKeyCredential # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings. # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens client = ChatCompletionsClient( endpoint = "https://models.inference.ai.azure.com", credential = AzureKeyCredential(os.environ["GITHUB_TOKEN"]), api_version = "2024-08-01-preview", ) response = client.complete( messages = [ SystemMessage(content = "Provide a detailed syllabus for a university-level mathematics course, including course objectives, weekly topics, assessment methods, and required materials.\n \nThe syllabus should cover all essential components such as the course title, description, prerequisites, learning outcomes, weekly schedules, and any relevant policies regarding attendance, grading, and participation.\n \n# Steps\n \n1. **Course Title and Description**: Clearly state the title of the course and provide a brief description of what the course will cover.\n2. **Prerequisites**: List any required courses or knowledge necessary for students to enroll.\n3. **Learning Outcomes**: Define what students are expected to learn by the end of the course.\n4. **Weekly Schedule**: Outline topics for each week, along with any associated readings or assignments.\n5. **Assessment Methods**: Describe how students will be evaluated (e.g., exams, quizzes, projects).\n6. **Required Materials**: Include information on textbooks and other resources needed for the course.\n7. **Course Policies**: State attendance, grading, and participation rules.\n \n# Output Format\n \nThe output should be formatted as a structured syllabus, presented in clear sections with headings for each part. The document should be detailed yet concise, ideally around 3-5 pages in length.\n \n# Examples\n \n**Example 1** \n**Input:** \nCreate a syllabus for a Calculus I course. \n**Output:** \n- **Course Title**: Calculus I \n- **Description**: An introduction to limits, derivatives, and integrals. \n- **Prerequisites**: Pre-Calculus or equivalent. \n- **Learning Outcomes**: Students will be able to calculate limits, differentiate basic functions, and understand the Fundamental Theorem of Calculus. \n- **Weekly Schedule**: \n - Week 1: Introduction to Limits \n - Week 2: Continuity \n - Week 3: Derivatives \n - ... \n- **Assessment Methods**: Midterm exam (30%), Final exam (40%), Weekly quizzes (20%), Participation (10%). \n- **Required Materials**: \"Calculus: Early Transcendentals\" by James Stewart. \n- **Course Policies**: Attendance required, late assignments will incur a penalty.\n \n**Example 2** \n**Input:** \nDesign a syllabus for a Linear Algebra course. \n**Output:** \n- **Course Title**: Linear Algebra \n- **Description**: Study vector spaces, matrices, and linear transformations. \n- **Prerequisites**: None. \n- **Learning Outcomes**: Mastery of matrix operations and ability to solve systems of linear equations. \n- **Weekly Schedule**: \n - Week 1: Introduction to Vector Spaces \n - Week 2: Matrix Operations \n - Week 3: Determinants \n - ... \n- **Assessment Methods**: Two midterms (50%), Homework assignments (30%), Attendance (20%). \n- **Required Materials**: \"Linear Algebra Done Right\" by Sheldon Axler. \n- **Course Policies**: Participation in class discussions is mandatory. \n \n# Notes\n \nEnsure that the syllabus is comprehensive and tailored to the specific course topic. Consider including any unique teaching methods or technologies that will be employed during the course."), UserMessage(content = [ TextContentItem(text = "Explain to me the Fourier equation in simple terms"), ]), ], model = "gpt-4o-mini", response_format = "text", max_tokens = 4096, temperature = 1, top_p = 1, ) print(response.choices[0].message.content) This Python code is ready to be modified and used in any Generative AI application. It can be modified with any Orchestration framework like Semantic Kernel to add more features or even make an agentic application. History Section: We also have the “History” and “New Prompt”. History shows all the previous sessions; we can revisit and resume working or perhaps check the output or regenerate the code. History” and “New Prompt” In essence, the Prompt Builder tool significantly streamlines the process of crafting effective prompts, saving developers valuable time. Beyond prompt creation, it also facilitates output evaluation, model behavior analysis, and generates quality code to accelerate application development. Stay tuned for upcoming blog posts, where we'll delve into even more advanced techniques for building powerful generative AI applications. You can also join our AI Sparks series to learn more about the capabilities of the AI Toolkit for Visual Studio Code.Fine-Tuning Language Models with Azure AI Foundry: A Detailed Guide
What is Azure AI Foundry? Azure AI Foundry is a comprehensive platform designed to simplify the development, deployment, and management of AI models. It provides a user-friendly interface and powerful tools that enable developers to create custom AI solutions without needing extensive machine learning expertise. Key Features of Azure AI Foundry One-Button Fine-Tuning: A streamlined process that allows users to fine-tune models with minimal configuration. Integration with Development Tools: Seamless integration with popular development environments, particularly Visual Studio Code. Support for Multiple Models: Access to a variety of pre-trained models, including the Phi family of models. Understanding Fine-Tuning Fine-tuning is the process of taking a pre-trained model and adapting it to a specific dataset or task. This is particularly useful when the base model has been trained on a large corpus of general data but needs to perform well on a narrower domain. Why Fine-Tune? Improved Performance: Fine-tuning can significantly enhance the model's accuracy and relevance for specific tasks. Reduced Training Time: Starting with a pre-trained model reduces the amount of data and time required for training. Customization: Tailor the model to meet the unique needs of your application or business. One-Button Fine-Tuning in Azure AI Foundry Step-by-Step Process Select the Model: Log in to Azure AI Foundry and navigate to the model selection interface. Choose Phi-3 or another small language model from the available options. Prepare Your Data: Ensure your dataset is formatted correctly. Typically, this involves having a set of input-output pairs that the model can learn from. Upload your dataset to Azure AI Foundry. The platform supports various data formats, making it easy to integrate your existing data. Initiate Fine-Tuning: Locate the one-button fine-tuning feature within the Azure AI Foundry interface. Click the button to start the fine-tuning process. The platform will handle the configuration and setup automatically. Monitor Progress: After initiating fine-tuning, you can monitor the process through the Azure portal. The portal provides real-time updates on training metrics, allowing you to track the model's performance as it learns. Evaluate the Model: Once fine-tuning is complete, evaluate the model's performance using a validation dataset. Azure AI Foundry provides tools for assessing accuracy, precision, recall, and other relevant metrics. Deploy the Model: After successful evaluation, you can deploy the fine-tuned model directly from Azure AI Foundry. The platform supports various deployment options, including REST APIs and integration with other Azure services. Using the AI Toolkit in Visual Studio Code Overview of the AI Toolkit The AI Toolkit for Visual Studio Code enhances the development experience by providing tools specifically designed for AI model management and fine-tuning. This integration allows developers to work within a familiar environment while leveraging powerful AI capabilities. Key Features of the AI Toolkit 1) Model Management: Easily manage and switch between different models, including Phi-3 and Ollama models. 2) Data Handling: Simplified data upload and preprocessing tools to prepare datasets for training. 3) Real-Time Collaboration: Collaborate with team members in real-time, sharing insights and progress on AI projects. How to Use the AI Toolkit 1) Install the AI Toolkit: Open Visual Studio Code and navigate to the Extensions Marketplace. Search for "AI Toolkit" and install the extension. 2) Connect to Azure AI Foundry: Once installed, configure the toolkit to connect to your Azure AI Foundry account. This will allow you to access your models and datasets directly from Visual Studio Code. 3) Fine-Tune Models: Use the toolkit to initiate fine-tuning processes directly from your development environment. Monitor training progress and view logs without leaving Visual Studio Code. 4) Consume Ollama Models: The AI Toolkit supports the consumption of Ollama models, providing additional flexibility in your AI projects. This feature allows you to integrate various models seamlessly, enhancing your application's capabilities. Microsoft ONNX Live for Fine-Tuning What is Microsoft ONNX Live? Microsoft ONNX Live is a platform that allows developers to deploy and optimize AI models using the Open Neural Network Exchange (ONNX) format. ONNX is an open-source format that enables interoperability between different AI frameworks, making it easier to deploy models across various environments. Key Features of Microsoft ONNX Live Model Optimization: ONNX Live provides tools to optimize models for performance, ensuring they run efficiently in production environments. Cross-Framework Compatibility: Models trained in different frameworks (like PyTorch or TensorFlow) can be converted to ONNX format, allowing for greater flexibility in deployment. Real-Time Inference: ONNX Live supports real-time inference, enabling applications to utilize AI models for immediate predictions. Fine-Tuning with ONNX Live Model Conversion: If you have a model trained in a different framework, you can convert it to ONNX format using tools provided by Microsoft. This conversion allows you to leverage the benefits of ONNX Live for deployment and optimization. Integration with Azure AI Foundry: Once your model is in ONNX format, you can integrate it with Azure AI Foundry for fine-tuning. The one-button fine-tuning feature can be used to adapt the ONNX model to your specific dataset. Optimization Techniques: After fine-tuning, you can apply various optimization techniques available in ONNX Live to enhance the model's performance. Techniques such as quantization and pruning can significantly reduce the model size and improve inference speed. Deployment: Once optimized, the model can be deployed directly from Azure AI Foundry or ONNX Live. This deployment can be done as a REST API, allowing easy integration with web applications and services. Additional Resources To further enhance your understanding and capabilities in fine-tuning language models, consider exploring the following resources: Phi-3 Cookbook: This comprehensive guide provides insights into getting started with Phi models, including best practices for fine-tuning and deployment. Explore the Phi-3 Cookbook. Ignite Fine-Tuning Workshop: This workshop offers a hands-on approach to learning about fine-tuning techniques and tools. It includes real-world scenarios to help you understand the practical applications of fine-tuning. Visit the GitHub Repository. Conclusion Fine-tuning language models like Phi-3 using Azure AI Foundry, combined with the AI Toolkit in Visual Studio Code and Microsoft ONNX Live, provides a powerful and efficient workflow for developers. The one-button fine-tuning feature simplifies the process, while the integration with ONNX Live allows for optimization and deployment flexibility. By leveraging these tools, you can enhance your AI applications, ensuring they are tailored to meet specific needs and perform optimally in production environments. Whether you are a seasoned AI developer or just starting, Azure AI Foundry and its associated tools offer a robust ecosystem for building and deploying advanced AI solutions. References Microsoft Docs Links Fine-Tuning Models in Azure OpenAI Azure AI Services Documentation Azure Machine Learning Documentation Microsoft Learn Links Develop Generative AI Apps in Azure Fine-Tune a Language Model Azure AI Foundry Overview Get started with AI Toolkit for Visual Studio Code
