Course Outline
Foundations and Reliable Use of GenAI
- Core concepts of AI and GenAI: understanding capabilities, mechanisms, value propositions, and limitations
- Effective prompting strategies: designing reusable structures, defining clear inputs, constraints, and output specifications
- Iterative refinement: optimizing results through feedback loops and precise instructions
- Quality assurance: utilizing checklists, cross-validation, assumption tracking, traceability, and acceptance criteria
- Standardizing outputs: creating templates for technical notes, summaries, reports, and action items
- Documentation and requirements management: drafting, editing, structuring, summarizing, and writing change requests
- Ethical usage and security: ensuring confidentiality, protecting IP, adhering to governance principles, and following safety protocols
- Practical exercises using realistic, anonymized scenarios
Applied Use Cases, Productivity, and Workflow Integration
- Advanced analysis and reporting: transforming raw data into structured insights and executive-level summaries
- Enhanced problem solving: leveraging AI for root cause analysis and strategic action planning
- Improved cross-functional communication: ensuring decision clarity, effective handovers, accurate meeting minutes, and stakeholder alignment
- AI-assisted development: safely generating and reviewing code snippets, pseudocode, and test logic
- Accelerating knowledge work: developing reusable procedures, internal standards, and knowledge-base assets
- Seamless workflow integration: establishing repeatable end-to-end processes with embedded validation steps
- Resource libraries: maintaining role-based prompt collections and checklists to ensure consistency and adoption
- Capstone project and adoption roadmap: translating individual case studies into repeatable workflows with defined quick wins and performance metrics
Requirements
This course targets professionals in engineering, technical, and operational roles who engage in documentation, process management, data-driven decision-making, and cross-team collaboration. It is ideal for specialists and team leaders seeking to enhance productivity and output quality through the integration of Generative AI into routine tasks, without necessitating advanced programming or data science expertise. Additionally, the training is highly beneficial for operational and business support personnel who require more precise, rapid, and consistent deliverables when handling technical information.
Testimonials (3)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
The training style, preparation quality and focus on the important/relevant points, good tips, opening for any question with complete answers, info share willing, overall the high know how of the trainer combined with the training method.
Teofil Laurentiu Sasu - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Almost everything !
