AI for Software Requirements and User Story Generation

• Basic understanding of software project lifecycles
• Familiarity with Agile or Scrum frameworks
• No technical background required

Audience

• Product owners
• Business analysts
• Scrum masters

AI for Software Requirements and User Story Generation is a practical course focused on using generative AI to transform stakeholder input into structured requirements, epics, user stories, and acceptance criteria suitable for Agile development workflows.

This instructor-led, live training (online or onsite) is aimed at beginner-level product and project professionals who wish to use tools like ChatGPT or Claude to improve clarity, speed, and traceability in requirement gathering and refinement.

By the end of this training, participants will be able to:

• Use AI prompts to gather and refine business requirements.
• Convert feature requests into well-structured user stories and epics.
• Generate acceptance criteria, edge cases, and definitions of done with AI assistance.
• Collaborate with development teams using AI-structured documentation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to AI for Requirements Engineering

• Overview of AI tools for product teams
• Understanding the role of requirements in Agile and Scrum
• Benefits and limitations of using AI for requirement capture

Gathering and Structuring Requirements with AI

• Interview simulation with AI: transforming verbal input into requirements
• Prompting techniques to clarify ambiguous statements
• Organizing requirements into themes and features

Generating User Stories and Epics

• Turning plain text into actionable user stories
• Using AI to identify actors, actions, and goals
• Creating epics and story hierarchies from AI suggestions

Writing Acceptance Criteria and Edge Cases

• Generating Given-When-Then testable criteria
• Identifying exception paths and boundary conditions with AI
• Reviewing AI outputs for clarity and completeness

Refinement and Story Grooming with AI

• Summarizing stakeholder meetings and notes
• Splitting and merging stories using prompt guidance
• Automating backlog refinement with AI assistance

Collaboration and Handoff

• Sharing AI-generated stories with developers
• Ensuring traceability from feature to test case
• Generating documentation for stakeholder sign-off

Summary and Next Steps