Course Outline
Foundations and Responsible Application of GenAI
- Core concepts of AI and GenAI: understanding capabilities, mechanisms, value-addition, and limitations
- Effective prompting strategies: developing reusable structures, defining clear inputs, constraints, and desired output formats
- Iterative refinement: optimizing results through feedback loops and structured guidance
- Ensuring output quality: implementing checklists, cross-verification, assumption tracking, traceability, and acceptance criteria
- Standardizing outputs: creating templates for technical notes, summaries, reports, and action items
- Documentation and requirement management: drafting, rewriting, structuring, summarizing, and capturing changes
- Ethical usage and data security: maintaining confidentiality, protecting IP, adhering to governance principles, and following safe-use protocols
- Practical exercises using realistic, anonymized scenarios
Applied Scenarios, Productivity Gains, and Workflow Integration
- Advanced analysis and reporting: transforming raw data into structured insights and executive-ready summaries
- Enhanced problem solving: leveraging AI for root cause analysis and strategic action planning
- Improved cross-functional communication: ensuring decision clarity, smooth handovers, accurate meeting minutes, and stakeholder alignment
- AI as a coding copilot: safely generating and reviewing code snippets, pseudocode, and test logic
- Accelerating knowledge work: developing reusable procedures, internal standards, and knowledge base entries
- Workflow integration: establishing repeatable end-to-end processes from initial request to final deliverable, including validation steps
- Prompt libraries and checklists: curating role-based collections to enhance consistency and adoption
- Capstone project and 30-day adoption strategy: converting individual practical cases into repeatable workflows, focusing on quick wins and simple metrics
Requirements
This training is tailored for professionals operating in engineering, technical, and operational settings who manage documentation, structured processes, data-informed decisions, and team collaboration. It is ideal for specialists and team leaders aiming to boost productivity and output quality by leveraging Generative AI in daily tasks, with no requirement for advanced programming or data science expertise. Additionally, the course benefits operational or business support roles that regularly engage with technical information and seek clearer, faster, and more consistent deliverables.
Testimonials (7)
The trainer's open and warm style, and the accessible language.
OPREAN ADELA - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
Adaptability and the fact that the instructor ensured that any confusion was explained and understood.
Rares Ursu - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
As I learned to correctly format a prompt
Alin Zoltan - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
The participant did not move to the next step until all learners had completed the task/exercise assigned. They explained and repeated the information whenever asked.
Drumia Radu - Gheorghe - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
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 !
