Dive into the fascinating world of AI Agents in this comprehensive learning path that transforms beginners into creators. You'll explore what agents truly are and discover the inner workings that make them tick, from decision-making algorithms to execution frameworks. The journey continues with an in-depth look at the tools agents leverage and tasks they excel at, preparing you for the hands-on lab where you'll build your very own agent from scratch. By the end, you'll have both theoretical knowledge and practical experience in creating these powerful digital assistants that are revolutionizing how we interact with technology and solve complex problems.

This learning path introduces AI users to key concepts and practices for crafting effective prompts that maximize the value of AI tools. It begins by providing essential background knowledge to explain why prompt engineering is critical for generating high-quality responses. It will then culminate with the introduction of the Prompt Blueprint—a practical framework to help users design effective prompts.

Welcome to the Microsoft Copilot Chat Learning Path—your gateway to mastering one of the most powerful AI tools designed to enhance productivity and streamline everyday tasks. Whether you're drafting documents, analyzing data, managing emails, or brainstorming ideas, Copilot Chat offers intelligent support right where you work. This learning path will introduce you to Copilot Chat, show you how to access it across various platforms, guide you through its core features, and help you build confidence in using prompts to get the most out of your conversations. Let's dive in and discover how Copilot Chat can transform the way you work.

This learning path teaches you to build LLM applications using LangChain's composable building blocks. Three foundational articles explain orchestration frameworks, LangChain's place among them, and its core abstractions—prompts, chains, and pipelines. Hands-on labs then let you assemble these primitives into increasingly sophisticated patterns: structured output, memory management, RAG, and agentic tool use.

Welcome to the How to Pick the Right LLM Learning Path! Designed for engineers and technical practitioners, this course moves beyond benchmark-driven defaults to build a repeatable approach to model selection, rooted in a core truth: there is no single best model, only the right model for a specific task. You will first master a framework built around six key selection factors: use case, performance, latency, cost at scale, deployment, and security. From there, the path dives into the distinct LLM call types that power agentic systems—including classification, planning, and tool dispatch—and explores how architectural properties like reasoning mode and structured-output compliance dictate fit. Finally, you will jump into a hands-on JupyterLab environment to benchmark models across four capability tiers on canonical agent tasks. By measuring real-world latency and token consumption, you will build a data-driven scorecard to confidently design optimized, multi-tier model architectures.