Learn/AI for Scrum & Agile Teams
Transform your Agile practice with AI-powered tools and strategies. Learn how to leverage generative AI to accelerate sprint planning, enhance team collaboration, and deliver value faster—without losing the human-centered principles that make Scrum work.
Practical applications of AI across the entire Scrum framework
Amplify your facilitation, coaching, and servant leadership with intelligent tools
Code smarter with AI-assisted development, testing, and continuous delivery
Customized journeys for ScrumMasters, Product Owners, and Developers
Transform your Scrum and Agile practices with AI-powered tools and techniques
Essential foundations and curated resources for AI in Agile teams
Structured progression from basics to advanced AI implementation
Get started with AI tools for sprint planning and retrospectives
Join the Generative AI for Scrum Teams Workshop
Stop wondering how AI fits into your Agile workflow. In this hands-on workshop, you'll learn exactly how to integrate AI tools into every sprint ceremony, backlog refinement session, and delivery cycle—without disrupting the Scrum framework that already works for your team.
Perfect for: Scrum Masters, Product Owners, Development Teams, and Agile Coaches who want to boost productivity while maintaining team collaboration and quality.
Taught by Rod Claar, Certified Scrum Trainer with 30+ years of development experience and specialized AI-Enhanced Scrum methodology.
A curated playlist of specific YouTube content.
24 Feb 2026
Prompt engineering is the skill of giving clear instructions to an AI so it can understand your goals and produce better results. Modern AI can act as an independent agent for longer-running work—so Scrum Masters benefit from structured communication: prompt craft, context engineering, intent engineering, and specification engineering.
Use these four layers together to reliably drive outcomes across facilitation, analysis, planning, and quality.
Write clear instructions so the model understands the task and produces actionable output.
Provide only the relevant background (notes, goals, constraints) so the model can reason correctly.
State the true goal—what “good” looks like—so the model optimizes for outcomes, not just text.
Define rules and output formats that hold up across long-running or multi-step tasks.
Structured prompts work best. XML-style tags help models separate context, intent, instructions, constraints, and formatting.
[Insert raw notes]
"Create a user login portal with email and social media options."
Title: Create Google Login Button. Description: Add a front-end button that links to the Google authentication API.
"As a customer, I want to filter search results by price so I can find affordable items."
Given / When / Then
The unified template is broadly effective, but you’ll get better results by tuning structure, context volume, and output guidance per model.
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