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Course Outline
- Foundations of AI-Native Requirements Engineering [THEORY & DEMO]
- Transitioning from traditional business analysis to an AI-native approach.
- Understanding the concepts of “compileable documentation” and “AI-readable requirements.”
- Practical foundations of the thesis: “Requirement quality equals code quality.”
- Producing structured analysis outputs through prompt engineering.
- Writing Use Cases and User Stories with Artificial Intelligence [HANDS-ON]
- AI-supported generation of Use Case diagrams and scenarios.
- Positioning artificial intelligence in the creation of User Stories, Acceptance Criteria, and edge case discovery.
- Iterative story refinement using AI in alignment with INVEST criteria.
- Application: Creating a complete set of stories from a real business requirement using AI.
- Positioning Artificial Intelligence as a Requirements Engineer [WORKSHOP]
- Utilizing AI to structure stakeholder interviews, prioritize requirements, and detect inconsistencies.
- PRD (Product Requirement Document) production pipeline: From briefing to final document.
- Designing AI-driven workflows for requirements traceability and impact analysis.
- AI-Assisted Prototyping Aligned with Brand Identity [LIVE DEMO]
- Creating a functional React prototype from a screenshot of an existing application using AI (Proto Cloner method).
- Iterative design while preserving brand colors, typography, and UI patterns.
- Demonstration: Transitioning from PRD to a working interface in 15 minutes.
- Data Modeling and Analysis with Artificial Intelligence [HANDS-ON]
- AI-supported transition from business requirements to entity-relationship diagrams.
- Automatic generation of data dictionaries, normalization, and relationship mappings.
- Using AI in API and database schema design.
- Analyzing and optimizing existing data structures with AI.
- Integration and Cross-Application [CAPSTONE PROJECT]
- Capstone project applying all modules: End-to-end execution of requirements gathering, documentation, prototyping, and data modeling using AI in a real-world business scenario.
- Team-based collaboration, presentations, and feedback processes.
Requirements
- A foundational understanding of business analysis, the software development life cycle (SDLC), or product development processes.
- Basic experience working with digital products, software projects, or data-driven systems.
- Familiarity with any programming language at a basic level (optional but beneficial).
Audience
- Business Analysts
- Product Managers and Product Owners
- Software developers, solution architects, and professionals working within digital product teams.
7 Hours
Testimonials (2)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
Michal Maj - XL Catlin Services SE (AXA XL)
Course - GitHub Copilot for Developers
Trainer able to adjust the course level during training to fit our understanding level on the topic, so that we could gain more useful knowledge that could further help us harness the tools in our daily works.
