Generative AI for Beginners - A 12-Lesson Course
Are you fascinated by the world of Artificial Intelligence and its endless possibilities? Are you a beginner eager to dive into the realm of Generative AI? If so, you're in the right place! In this blog post, we're excited to introduce you to a comprehensive 12-lesson course designed to teach you everything you need to know to start building Generative AI applicationsMicrosoft Semantic Kernel and AutoGen: Open Source Frameworks for AI Solutions
Explore Microsoft’s open-source frameworks, Semantic Kernel and AutoGen. Semantic Kernel enables developers to create AI solutions across various domains using a single Large Language Model (LLM). AutoGen, on the other hand, uses AI Agents to perform smart tasks through agent dialogues. Discover how these technologies serve different scenarios and can be used to build powerful AI applications.Microsoft Learn Launches a New Series on Generative AI for Innovators
Do you have a great idea for a startup, but don’t know how to turn it into reality? Do you want to learn how to use the latest AI technologies to create innovative products and services? If yes, then you should check out the new series on Microsoft Learn: Generative AI for Innovators. This series will teach you how to use powerful AI tools such as GPT-4 and DALL-E, to generate novel and creative solutions for various domains and challenges. You will also learn how to use AI to quickly prototype and test your product, and how to build a sustainable and profitable business model for your startup. Whether you are a beginner or an expert, this series will help you unleash your entrepreneurial potential and make your startup dreams come true. Don’t miss this opportunity to learn from the best and join the generative AI revolution!Visual Studio Code AI Toolkit: Run LLMs locally
AI Toolkit is here Getting the LLMs/ SLMs on our local machines. This toolkit lets us easily download the models on our local machine. Evaluation of the model. Whenever we need to evaluate a model to check for the feasibility to any particular application, then this tool lets us do it in a playground environment, which is what we will seeing in this blog. Fine-tuning, this majorly delas with training the model further to do the tasks that we specifically want the model to do. Usually, it does a generic task and has generic data, with fine-tuning we can give it a particular flavor to perform particular task.Navigating the Future with Microsoft Copilot
A Guide for Technical Students Copilot is an AI assistant powered by language models, which offers innovative solutions across the Microsoft Cloud. Find what you, a technical professional, need to enhance your productivity, creativity, and data accessibility, and make the most of the enterprise-grade data security and privacy features for your organization.Optimizing Retrieval for RAG Apps: Vector Search and Hybrid Techniques
In this blog we are going to dive into optimizing our search strategy with Hybrid search techniques. Common practices for implementing the retrieval step in retrieval-augmented generation (RAG) applications are; Keyword search Vector Search Hybrid search (Keyword + Vector) Hybrid + Semantic rankerBuild 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.
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.
