Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
- AI-Native Requirements Engineering Fundamentals [THEORY & DEMO]
- Transition from traditional business analysis to the AI-native approach.
- Concepts of "compilable documents" and "AI-readable requirements".
- Practical foundations of the thesis "Requirements quality = Code quality".
- Structured analysis output generation using prompt engineering.
- Writing Use Cases and User Stories with Artificial Intelligence [PRACTICAL]
- AI-assisted generation of Use Case diagrams and scenarios.
- The role of AI in discovering User Stories, Acceptance Criteria, and edge cases.
- Iterative story improvement (story refinement) aligned with INVEST criteria using AI.
- Application: Creating a complete set of user 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 detect inconsistencies.
- PRD (Product Requirement Document) production pipeline: From briefing to documentation.
- AI workflow design for requirements traceability and impact analysis.
- AI-Assisted Prototyping Tailored to 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: 15-minute demonstration of transitioning from requirements to a functional interface.
- Data Modeling and Analysis with Artificial Intelligence [PRACTICAL]
- AI-assisted transition from business requirements to entity-relationship diagrams.
- Automated generation of data dictionaries, normalization, and relationship maps.
- Utilizing AI in API and database schema design.
- Analysis and optimization of existing data structures using AI.
- Integration and Cross-Application [CAPSTONE PROJECT]
- Capstone project applying all modules together: End-to-end execution of requirements gathering, documentation, prototyping, and data modeling using AI within a real business scenario.
- Team-based work, presentations, and feedback processes.
Requirements
- A basic understanding of business analysis, software development lifecycle (SDLC), or product development processes.
- Fundamental experience working with digital products, software projects, or data-centric systems.
- Basic experience working with any programming language (not mandatory but beneficial).
Audience
- Business Analysts
- Product Managers and Product Owners
- Software developers, solution architects, and professionals working in 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
