Microsoft’s POML Explained With a Simple Example! (+Ollama)

Unlock the power of Microsoft POML (Prompt Orchestration Markup Language) — a standardized way to write prompts for AI, just like HTML standardized the web. Instead of messy, inconsistent text prompts, POML lets you structure your instructions with clear tags for roles, tasks, data sources, and output formats, making your AI interactions reliable and repeatable. In this step-by-step tutorial, I’ll show you how to: ✅ Create your first POML file from scratch ✅ Use the VS Code extension to preview prompts instantly ✅ Embed CSV, JSON, and multimedia data directly into prompts without copy-paste chaos ✅ Build a simple Python app that loads POML, merges messages, and sends it to Ollama ✅ Run prompts on both text-based and vision-language models for richer results We’ll start simple with an orders Q&A example and then move to an advanced financial analysis case that integrates spreadsheets and images. By the end, you’ll see how POML makes working with AI models structured, efficient, and future-proof. If you’ve ever struggled with inconsistent AI outputs, POML is your new secret weapon. 🔗 Code + Examples: https://github.com/microsoft/poml/tree/main/examples https://github.com/microsoft/poml/ https://mer.vin/2025/08/microsoft-poml-beginners-tutorial/ 👍 Like & Subscribe for more AI tutorials! 0:00 – What is POML? 1:44 – Creating your first POML file 2:24 – Adding CSV + JSON data 4:22 – Loading POML in Python 5:14 – Calling Ollama with structured prompts 6:27 – Advanced example with images 7:31 – Wrap-up & next steps This video introduces Microsoft POML, a new **open source ai** tool designed to standardize the way we interact with **large language models**. By providing a structured approach to prompts, POML ensures consistency and reduces errors, improving the overall effectiveness of **artificial intelligence**. Check out this **ai news** to stay up to date on the latest **ai tools**!