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Course Outline
- Fundamentals of AI-Native Requirements Engineering [THEORY & DEMO]
- Transitioning from traditional business analysis to an AI-native approach.
- Concepts of 'compilable documents' and 'AI-readable requirements.'
- Practical foundations of the thesis: 'Requirement quality = Code quality.'
- Generating structured analysis outputs through prompt engineering.
- Writing Use Cases and User Stories with Artificial Intelligence [PRACTICAL]
- AI-supported production of Use Case diagrams and scenarios.
- The role of AI in discovering User Stories, Acceptance Criteria, and edge cases.
- Iterative story refinement aligned with INVEST criteria using AI.
- Exercise: Generating a complete set of stories from a real business requirement using AI.
- Positioning Artificial Intelligence as a Requirements Engineer [WORKSHOP]
- Using AI to structure stakeholder interviews, prioritize requirements, and identify inconsistencies.
- Product Requirement Document (PRD) production pipeline: From briefing to documentation.
- Designing AI workflows for requirement traceability and impact analysis.
- AI-Powered Prototyping Aligned with Brand Identity [LIVE DEMO]
- Creating a functional React prototype from an existing application screenshot using AI (Proto Cloner method).
- Iterative design while preserving brand colors, typography, and UI patterns.
- From PRD to prototype: A 15-minute demo transitioning from requirements to a working interface.
- Data Modeling and Analysis with Artificial Intelligence [PRACTICAL]
- AI-supported transition from business requirements to entity-relationship diagrams.
- Automatic production of data dictionaries, normalization, and relationship maps.
- Utilizing 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 together: End-to-end requirement gathering, documentation, prototyping, and data modeling using AI within a real-world business scenario.
- Team-based work, presentations, and feedback processes.
Requirements
- A foundational understanding of business analysis, software development lifecycle (SDLC), or product development processes.
- Basic experience working with digital products, software projects, or data-centric systems.
- Basic-level working experience with any programming language (not mandatory but beneficial).
Audience
- Business Analysts
- Product Managers and Product Owners
- Professionals working in software development teams, solution architecture, and digital product teams.
7 Hours
Testimonials (1)
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
