Tag:"tips and tricks" | Microsoft Community Hub

Excel Formula, combining two IF statements
I need to string together two IF statements, =IF(C7>=70, C7*0.5) and =IF(C7<=69, C7*0.65), please help
Rosa Hernandez
May 03, 2025 Place Excel
92KViews
1like
40Comments
Boost Your Productivity: How to Automate and Schedule Prompts in Copilot
In this tutorial, I'll show you how to automate and schedule prompts in Copilot to maximize your productivity. Whether you're a seasoned user or new to Copilot, this guide will help you streamline your workflow and save valuable time. In this video, you'll learn: - The basics of setting up automated prompts in Copilot. - How to schedule prompts to fit your daily routine. - Tips and tricks to enhance your efficiency with Copilot. Don't forget to like, subscribe, and hit the notification bell to stay updated with our latest productivity hacks! #AI #Copilot #Automate #MVPbuzz #Microsoft365Copilot
GiulianoDeLuca
May 02, 2025 Place Microsoft 365 Copilot
659Views
3likes
10Comments
Quick fixes to boost your Copilot responses
Did Copilot disappoint you recently? Who has never complained about Copilot responses? Myself included. You know that moment when you try to get a good Copilot response, and all you get is vague one? Yep. Been there. It’s Not You, It's not Copilot either, It's Your Prompt People tend to treat Copilot like a search engine, but Copilot isn’t a search engine. I know it feels like it should be—type in your query, get a polished answer. Here's the deal: Copilot (and all other LLMs) work with recognizing patterns and context based on your prompts. Copilot does not retrieve information, but generates answers. Copilot relies on the context and specificity of your prompts to generate useful responses. That means vague, lazy prompts are setting it up to give vague responses. If you want great output, you need great input. So let’s talk about how to give Copilot the kind of prompt it can actually work with. 10 Quick Fixes to Boost Your Copilot Responses Write clear prompts: ❌ “Make it better” → ✅ “Summarize in 3 points” Be specific: ❌ “Write an email” → ✅ “Draft a friendly reminder email for project deadline” Be direct: ❌ “Could you try to…” → ✅ “Generate a bulleted list of main takeaways” Give context: ❌ “Draft an email” → ✅ “Draft a response to the client email /<youremail> to apologyse for the delivery delays” Divide to conquer: ❌ “Write a report” → ✅ “Outline the report in sections: intro, analysis, and conclusion” Choose wisely: ❌ Copilot is not equal Copilot. → ✅ Understand the differences between M365 Copilot Chat and M365 Copilot and learn when to use what. I wrote a post about this topic already, here you can find it: Copilot Chats vsus. M365 Copilot: What's the difference? Try rephrasing: ❌ “Try again” → ✅ “Rephrase the sentence” Sometimes, a small word change makes a big difference. Iterate: Unhappy? ✅ If your plan A did not work, go for plan B. You can start a new chat, adjust your initial prompt, and try again! Include web content when relevant: ✅ Enrich your Copilot responses with web content. Toggle on web content like this: Click on the 3 elipses on the top, right corner of Copilot chat in Work mode. Give it another shot! ✅ LLMs are evolving quickly. What it did not work last month, it may work today! Want Copilot to stop disappointing you? Prompt like you mean it. 💡 Got your own favorite Copilot trick? Share it in the comments below.
AmeliaHernandez
May 02, 2025 Place Microsoft 365 Copilot
76Views
2likes
2Comments
Make Phi-4-mini-reasoning more powerful with industry reasoning on edge devices
In situations with limited computing, Phi-4-mini-reasoning will is an excellent model choice. We can use Microsoft Olive or Apple MLX Framework to quantize Phi-4-mini-reasoning and deploy it on edge terminals such as IoT, Laotop and mobile devices. Quantization In order to solve the problem that the model is difficult to deploy directly to specific hardware, we need to reduce the complexity of the model through model quantization. Undertaking the quantization process will inevitably cause precision loss. Quantize Phi-4-mini-reasoning using Microsoft Olive Microsoft Olive is an AI model optimization toolkit for ONNX Runtime. Given a model and target hardware, Olive (short for Onnx LIVE) will combine the most appropriate optimization techniques to output the most efficient ONNX model for inference in the cloud or on the edge. We can combine Microsoft Olive and Phi-4-mini-reasoning on Azure AI Foundry's Model Catalog to quantize Phi-4-mini-reasoning to an ONNX format model. Create your Notebook on Azure ML Install Microsoft Olive pip install git+https://github.com/Microsoft/Olive.git Quantize using Microsoft Olive olive auto-opt --model_name_or_path {Azure Model Catalog path ,such as azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1 }--device cpu --provider CPUExecutionProvider --use_model_builder --precision int4 --output_path ./phi-4-14b-reasoninig-onnx --log_level 1 Register your quantized Model ! python -m mlx_lm.generate --model ./phi-4-mini-reasoning --adapter-path ./adapters --max-token 4096 --prompt "A 54-year-old construction worker with a long history of smoking presents with swelling in his upper extremity and face, along with dilated veins in this region. After conducting a CT scan and venogram of the neck, what is the most likely diagnosis for the cause of these symptoms?" --extra-eos-token "<|end|>" Download to local and run Download the onnx model to local device ml_client.models.download("phi-4-mini-onnx-int4-cpu", 1) Running onnx model with onnxruntime-genai Install onnxruntime-genai (This is CPU version) pip install onnxruntime-genai Run it import onnxruntime_genai as og model_folder = "Your ONNX Model Path" model = og.Model(model_folder) tokenizer = og.Tokenizer(model) tokenizer_stream = tokenizer.create_stream() search_options = {} search_options['max_length'] = 32768 chat_template = "<|user|>{input}<|end|><|assistant|>" text = 'A school arranges dormitories for students. If each dormitory accommodates 5 people, 4 people cannot live there; if each dormitory accommodates 6 people, one dormitory only has 4 people, and two dormitories are empty. Find the number of students in this grade and the number of dormitories.' prompt = f'{chat_template.format(input=text)}' input_tokens = tokenizer.encode(prompt) params = og.GeneratorParams(model) params.set_search_options(**search_options) generator = og.Generator(model, params) generator.append_tokens(input_tokens) while not generator.is_done(): generator.generate_next_token() new_token = generator.get_next_tokens()[0] print(tokenizer_stream.decode(new_token), end='', flush=True) Get Notebook from Phi Cookbook : https://aka.ms/phicookbook Quantize Phi-4-mini-reasoning model using Apple MLX Install Apple MLX Framework pip install -U mlx-lm Convert Phi-4-mini-reasoning model through Apple MLX quantization python -m mlx_lm.convert --hf-path {Phi-4-mini-reasoning Hugging face id} -q Run Phi-4-mini-reasoning with Apple MLX in terminal python -m mlx_lm.generate --model ./mlx_model --max-token 2048 --prompt "A school arranges dormitories for students. If each dormitory accommodates 5 people, 4 people cannot live there; if each dormitory accommodates 6 people, one dormitory only has 4 people, and two dormitories are empty. Find the number of students in this grade and the number of dormitories." --extra-eos-token "<|end|>" --temp 0.0 Fine-tuning We can fine-tune the CoT data of different scenarios to enable Phi-4-mini-reasoning to have reasoning capabilities for different scenarios. Here we use the Medical CoT data from a public Huggingface datasets as our example (this is just an example. If you need rigorous medical reasoning, please seek more professional data support) We can fine-tune our CoT data in Azure ML Fine-tune Phi-4-mini-reasoning using Microsoft Olive in Azure ML Note- Please use Standard_NC24ads_A100_v4 to run this sample Get Data from Hugging face datasets pip install datasets run this script to get train data from datasets import load_dataset def formatting_prompts_func(examples): inputs = examples["Question"] cots = examples["Complex_CoT"] outputs = examples["Response"] texts = [] for input, cot, output in zip(inputs, cots, outputs): text = prompt_template.format(input, cot, output) + "<|end|>" # text = prompt_template.format(input, cot, output) + "<|endoftext|>" texts.append(text) return { "text": texts, } # Create the English dataset dataset = load_dataset("FreedomIntelligence/medical-o1-reasoning-SFT","en", split = "train",trust_remote_code=True) dataset = dataset.map(formatting_prompts_func, batched = True,remove_columns=["Question", "Complex_CoT", "Response"]) dataset.to_json("en_dataset.jsonl") Fine-tuning with Microsoft Olive olive finetune \ --method lora \ --model_name_or_path {Azure Model Catalog path , azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1} \ --trust_remote_code \ --data_name json \ --data_files ./en_dataset.jsonl \ --train_split "train[:16000]" \ --eval_split "train[16000:19700]" \ --text_field "text" \ --max_steps 100 \ --logging_steps 10 \ --output_path {Your fine-tuning save path} \ --log_level 1 Convert the model to ONNX with Microsoft Olive olive capture-onnx-graph \ --model_name_or_path {Azure Model Catalog path , azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1} \ --adapter_path {Your fine-tuning adapter path} \ --use_model_builder \ --output_path {Your save onnx path} \ --log_level 1 olive generate-adapter \ --model_name_or_path {Your save onnx path} \ --output_path {Your save onnx adapter path} \ --log_level 1 Run the model with onnxruntime-genai-cuda Install onnxruntime-genai-cuda SDK import onnxruntime_genai as og import numpy as np import os model_folder = "./models/phi-4-mini-reasoning/adapter-onnx/model/" model = og.Model(model_folder) adapters = og.Adapters(model) adapters.load('./models/phi-4-mini-reasoning/adapter-onnx/model/adapter_weights.onnx_adapter', "en_medical_reasoning") tokenizer = og.Tokenizer(model) tokenizer_stream = tokenizer.create_stream() search_options = {} search_options['max_length'] = 200 search_options['past_present_share_buffer'] = False search_options['temperature'] = 1 search_options['top_k'] = 1 prompt_template = """<|user|>{}<|end|><|assistant|><think>""" question = """ A 33-year-old woman is brought to the emergency department 15 minutes after being stabbed in the chest with a screwdriver. Given her vital signs of pulse 110\/min, respirations 22\/min, and blood pressure 90\/65 mm Hg, along with the presence of a 5-cm deep stab wound at the upper border of the 8th rib in the left midaxillary line, which anatomical structure in her chest is most likely to be injured? """ prompt = prompt_template.format(question, "") input_tokens = tokenizer.encode(prompt) params = og.GeneratorParams(model) params.set_search_options(**search_options) generator = og.Generator(model, params) generator.set_active_adapter(adapters, "en_medical_reasoning") generator.append_tokens(input_tokens) while not generator.is_done(): generator.generate_next_token() new_token = generator.get_next_tokens()[0] print(tokenizer_stream.decode(new_token), end='', flush=True) inference model with onnxruntime-genai cuda olive finetune \ --method lora \ --model_name_or_path {Azure Model Catalog path , azureml://registries/azureml/models/Phi-4-mini-reasoning/versions/1} \ --trust_remote_code \ --data_name json \ --data_files ./en_dataset.jsonl \ --train_split "train[:16000]" \ --eval_split "train[16000:19700]" \ --text_field "text" \ --max_steps 100 \ --logging_steps 10 \ --output_path {Your fine-tuning save path} \ --log_level 1 Fine-tune Phi-4-mini-reasoning using Apple MLX locally on MacOS Note- we recommend that you use devices with a minimum of 64GB Memory and Apple Silicon devices Get the DataSet from Hugging face datasets pip install datasets run this script to get train and valid data from datasets import load_dataset prompt_template = """<|user|>{}<|end|><|assistant|><think>{}</think>{}<|end|>""" def formatting_prompts_func(examples): inputs = examples["Question"] cots = examples["Complex_CoT"] outputs = examples["Response"] texts = [] for input, cot, output in zip(inputs, cots, outputs): # text = prompt_template.format(input, cot, output) + "<|end|>" text = prompt_template.format(input, cot, output) + "<|endoftext|>" texts.append(text) return { "text": texts, } dataset = load_dataset("FreedomIntelligence/medical-o1-reasoning-SFT","en", trust_remote_code=True) split_dataset = dataset["train"].train_test_split(test_size=0.2, seed=200) train_dataset = split_dataset['train'] validation_dataset = split_dataset['test'] train_dataset = train_dataset.map(formatting_prompts_func, batched = True,remove_columns=["Question", "Complex_CoT", "Response"]) train_dataset.to_json("./data/train.jsonl") validation_dataset = validation_dataset.map(formatting_prompts_func, batched = True,remove_columns=["Question", "Complex_CoT", "Response"]) validation_dataset.to_json("./data/valid.jsonl") Fine-tuning with Apple MLX python -m mlx_lm.lora --model ./phi-4-mini-reasoning --train --data ./data --iters 100 Running the model ! python -m mlx_lm.generate --model ./phi-4-mini-reasoning --adapter-path ./adapters --max-token 4096 --prompt "A 54-year-old construction worker with a long history of smoking presents with swelling in his upper extremity and face, along with dilated veins in this region. After conducting a CT scan and venogram of the neck, what is the most likely diagnosis for the cause of these symptoms?" --extra-eos-token "<|end|>" Get Notebook from Phi Cookbook : https://aka.ms/phicookbook We hope this sample has inspired you to use Phi-4-mini-reasoning and Phi-4-reasoning to complete industry reasoning for our own scenarios. Related resources Phi4-mini-reasoning Tech Report https://aka.ms/phi4-mini-reasoning/techreport Phi-4-Mini-Reasoning technical Report· microsoft/Phi-4-mini-reasoning Phi-4-mini-reasoning on Azure AI Foundry https://aka.ms/phi4-mini-reasoning/azure Phi-4 Reasoning Blog https://aka.ms/phi4-mini-reasoning/blog Phi Cookbook https://aka.ms/phicookbook Showcasing Phi-4-Reasoning: A Game-Changer for AI Developers | Microsoft Community Hub Models Phi-4 Reasoning https://huggingface.co/microsoft/Phi-4-reasoning Phi-4 Reasoning Plus https://huggingface.co/microsoft/Phi-4-reasoning-plus Phi-4-mini-reasoning Hugging Face https://aka.ms/phi4-mini-reasoning/hf Phi-4-mini-reasoning on Azure AI Foundry https://aka.ms/phi4-mini-reasoning/azure Microsoft (Microsoft) Models on Hugging Face Phi-4 Reasoning Models Azure AI Foundry Models Access Phi-4-reasoning models Phi Models at Azure AI Foundry Models Phi Models on Hugging Face Phi Models on GitHub Marketplace Models
Lee_Stott
May 01, 2025 Place Microsoft Developer Community Blog
758Views
1like
1Comment
Windows news you can use: April 2025
Catch up on the latest capabilities for Windows 11 management and security.
ThomasTrombley
Apr 30, 2025 Place Windows IT Pro Blog
1KViews
2likes
0Comments
An Update on Bicep Azure Verified Modules for Platform Landing Zone (ALZ)
But first some history and context As you may of heard in one of our Azure Landing Zone (ALZ) community calls over the past year, across ALZ we have been working hard to refactor both our Terraform and Bicep implementation options to be built upon Azure Verified Modules (AVM). Earlier this year we announced that the work for Terraform, which we started on first, was complete; and you can read more about that in the announcement blog post we posted here. But whilst this work was going on the ALZ Bicep team where already busy planning how they would go about doing the same and rebuilding ALZ Bicep from AVM modules. You can see the original plans and where we also asked for feedback in the GitHub issue (#791) . Enough history, what's the latest? Now to answer the question everyone has and rightly so 😁 Well, it's good news! We have been busy working away on getting a number of the AVM Bicep Resource Modules updated with missing bits and pieces that we need from an ALZ perspective. All fairly minor in most cases but some required some bigger updates than others, and some modules didn't exist at all so we have had to propose, create, and publish those of which we are pretty much done with 👍 We are still working towards an end of Q4 (June/July) target for a preview release of all the modules, accelerator and guidance on how to use the new version of ALZ Bicep, which will be called "Bicep Azure Verified Modules for Platform Landing Zone (ALZ)"; this is to align with Terraform and also to provide clear distinction between ALZ Bicep and the new AVM based version. Please note that the timeline shared above is an ETA and may move Announcing the preview release of `avm/ptn/alz/empty` AVM Pattern Module Before we get to a more complete release of all the required resources and modules to build the entire ALZ architecture with the new Bicep Azure Verified Modules for Platform Landing Zone (ALZ), we wanted to share an early look at the module that will be at the heart of all of your ALZ deployments. That module is called `avm/ptn/alz/empty` and is available in the Public Bicep Registry for you to try out today (currently version `0.1.0`)! Tip: Checkout the "max" test in the tests directory for advanced usage examples! module testMg 'br/public:avm/ptn/alz/empty:0.1.0' = { params: { managementGroupName: 'test-mg' // Other parameters here... } } This module is 1 of 11 modules that will all be based off the same code. The module optionally creates all of the below: The Management Group itself Can also target an existing Management Group Management Group Subscription Associations RBAC Custom Role Definitions RBAC Role Assignments Policy Assignments Custom Policy Definitions Custom Policy Set Definitions (Initiatives) There will also be 1 x Bicep Azure Verified Modules for Platform Landing Zone (ALZ) pattern module for each of the ALZ Architectures Management Groups, plus this empty one for custom and advanced scenarios. A reminder of those Management Groups and the associated modules that will be created for each of them: `avm/ptn/alz/int-root` `avm/ptn/alz/platform` `avm/ptn/alz/platform-management` `avm/ptn/alz/platform-identity` `avm/ptn/alz/platform-connectivity` `avm/ptn/alz/landing-zones` `avm/ptn/alz/landing-zones-corp` `avm/ptn/alz/landing-zones-online` `avm/ptn/alz/decommissioned` `avm/ptn/alz/sandbox` These Management Group aligned pattern modules will create the same resources as above, but will have the latest release of the ALZ Library baked in to each of the modules. Meaning that for the `avm/ptn/alz/int-root` pattern module, you won't have to declare all of the ALZ RBAC Custom Role Definitions, Custom Policy Definitions, Policy Assignments etc. via the input parameters as they'll be hardcoded in the module based off the latest release from the ALZ Library at the point the version of the module was released. This means that to build the ALZ Management Group hierarchy and make all of the default ALZ policy assignments, as documented here, you'd need a bicep file that would look something like this as a starting point: Important: None of these modules exist below today! module intRootMg 'br/public:avm/ptn/alz/int-root:0.1.0' = { params: { managementGroupName: 'int-root-mg' } } module platformMg 'br/public:avm/ptn/alz/platform:0.1.0' = { params: { managementGroupName: 'platform-mg' managementGroupParentId: intRootMg.outputs.managementGroupId } } module platformConnectivityMg 'br/public:avm/ptn/alz/platform-connectivity:0.1.0' = { params: { managementGroupName: 'platform-mg' managementGroupParentId: platformMg.outputs.managementGroupId } } This will make getting the ALZ Architecture out of the box really fast, and also really easy to upgrade and get the latest updates, by just bumping the version number as you desire when you are ready. Coupled with the `avm/ptn/alz/empty` module to add your own additional Policy Definitions and assignments, etc. at the same Management Groups scopes also helps you decouple the constant updates to the ALZ architecture and policies etc. from your own additional requirements. Helping you keep your code cleaner and our modules simple to maintain as we won't have to cater for handling additional custom definitions and assignments alongside the defaults from ALZ that are baked into the modules. Note: We are looking at suggesting that all of these are deployed via Deployment Stacks to help with lifecycle management of resources. e.g. help clean-up resources as well as deploy new ones; think policy assignments and definitions etc. We need to complete a lot more testing on this, but would love your feedback on experiences if you have any using Deployment Stacks to manage these kind of resources today. Open an issue/discussion on the ALZ Bicep GitHub repo 👍 Our asks to you 🫵 Please go try out and test the new `avm/ptn/alz/empty` module and test it out for all the scenarios you can think of relating to Management Groups, RBAC, Policies etc. we want to make sure it's "match fit/ready" before we then build the Management Group aligned modules and bake in the ALZ defaults to them. So please go and put the module through its paces and test it out. Tip: Checkout the "max" test in the tests directory for advanced usage examples! If you find any issues, bugs, feature requests or just have a question on how to use it, please just raise them as GitHub issues here (make sure to select the `avm/ptn/alz/empty` module from the drop down 👍) Thanks in advance for all your efforts and assistance and we look forward to hearing and getting your feedback on the module 👏
jtracey93msft
Apr 30, 2025 Place Azure Tools Blog
650Views
3likes
1Comment
Copilot in Outlook
One of my users trying to extract all calendar items from their Outlook through out the year to an excel to be created by Copilot. 1-When prompting the Excel file is generated but not clickable looks like corrupted. 2-When prompting Copilot to list all calendar items through out the year it shows only few results but not the full list of meetings. Anyone has an idea please
kh-7
Apr 29, 2025 Place Microsoft 365 Copilot
23Views
0likes
1Comment
Five Tips for Starting a New Developer User Group
Thinking about starting a user group or meetup? Here are five tips to help.
toddanglin
Apr 29, 2025 Place Microsoft Developer Community Blog
6.8KViews
2likes
7Comments
Build AI Agents with MCP Tool Use in Minutes with AI Toolkit for VSCode
We’re excited to announce Agent Builder, the newest evolution of what was formerly known as Prompt Builder, now reimagined and supercharged for intelligent app development. This powerful tool in AI Toolkit enables you to create, iterate, and optimize agents—from prompt engineering to tool integration—all in one seamless workflow. Whether you're designing simple chat interactions or complex task-performing agents with tool access, Agent Builder simplifies the journey from idea to integration. Why Agent Builder? Agent Builder is designed to empower developers and prompt engineers to: 🚀 Generate starter prompts with natural language 🔁 Iterate and refine prompts based on model responses 🧩 Break down tasks with prompt chaining and structured outputs 🧪 Test integrations with real-time runs and tool use such as MCP servers 💻 Generate production-ready code for rapid app development And a lot of features are coming soon, stay tuned for: 📝 Use variables in prompts �� Run agent with test cases to test your agent easily 📊 Evaluate the accuracy and performance of your agent with built-in or your custom metrics ☁️ Deploy your agent to cloud Build Smart Agents with Tool Use (MCP Servers) Agents can now connect to external tools through MCP (Model Control Protocol) servers, enabling them to perform real-world actions like querying a database, accessing APIs, or executing custom logic. Connect to an Existing MCP Server To use an existing MCP server in Agent Builder: In the Tools section, select + MCP Server. Choose a connection type: Command (stdio) – run a local command that implements the MCP protocol HTTP (server-sent events) – connect to a remote server implementing the MCP protocol If the MCP server supports multiple tools, select the specific tool you want to use. Enter your prompts and click Run to test the agent's interaction with the tool. This integration allows your agents to fetch live data or trigger custom backend services as part of the conversation flow. Build and Scaffold a New MCP Server Want to create your own tool? Agent Builder helps you scaffold a new MCP server project: In the Tools section, select + MCP Server. Choose MCP server project. Select your preferred programming language: Python or TypeScript. Pick a folder to create your server project. Name your project and click Create. Agent Builder generates a scaffolded implementation of the MCP protocol that you can extend. Use the built-in VS Code debugger: Press F5 or click Debug in Agent Builder Test with prompts like: System: You are a weather forecast professional that can tell weather information based on given location. User: What is the weather in Shanghai? Agent Builder will automatically connect to your running server and show the response, making it easy to test and refine the tool-agent interaction. AI Sparks from Prototype to Production with AI Toolkit Building AI-powered applications from scratch or infusing intelligence into existing systems? AI Sparks is your go-to webinar series for mastering the AI Toolkit (AITK) from foundational concepts to cutting-edge techniques. In this bi-weekly, hands-on series, we’ll cover: 🚀SLMs & Local Models – Test and deploy AI models and applications efficiently on your own terms locally, to edge devices or to the cloud 🔍 Embedding Models & RAG – Supercharge retrieval for smarter applications using existing data. 🎨 Multi-Modal AI – Work with images, text, and beyond. 🤖 Agentic Frameworks – Build autonomous, decision-making AI systems. Watch on Demand Share your feedback  Get started with the latest version, share your feedback, and let us know how these new features help you in your AI development journey. As always, we’re here to listen, collaborate, and grow alongside our amazing user community.  Thank you for being a part of this journey—let’s build the future of AI together! Join our Microsoft Azure AI Foundry Discord channel to continue the discussion 🚀
junjieli
Apr 29, 2025 Place Microsoft Developer Community Blog
1.5KViews
1like
0Comments
Copilot for MVP
I need to know that copilot is available for specific MVP category or all ? because it's show me in my MVP benefits but i am not authorize to use it.
Salamat_Shah
Apr 27, 2025 Place Microsoft 365 Copilot
82Views
0likes
5Comments