Course Outline
Foundations and Reliable Use of GenAI
- AI and GenAI fundamentals: understanding the technology, its mechanics, its value-add areas, and its limitations
- Practical prompting: developing reusable prompt frameworks, defining clear inputs, constraints, and output specifications
- Iteration strategies: optimizing results through feedback loops and precise instructions
- Output quality and verification: implementing checklists, cross-validation, assumption analysis, traceability, and acceptance criteria
- Standardizing deliverables: creating templates for technical notes, summaries, reports, and action items
- Documentation and requirements: drafting, revising, organizing, summarizing, and managing change/requirement documentation
- Responsible use and data security: ensuring confidentiality, IP protection, governance adherence, and safe usage protocols
- Hands-on exercises using realistic, anonymized scenarios
Applied Use Cases, Productivity, and Workflow Integration
- Analysis and reporting: transforming raw data into structured insights and executive-level summaries
- Problem solving and troubleshooting: leveraging AI for root cause analysis and action planning
- Cross-functional communication: enhancing decision clarity, handovers, meeting minutes, and stakeholder alignment
- AI as a copilot for code and automation: safely generating and reviewing code snippets, pseudocode, and test logic
- Accelerating knowledge work: developing reusable procedures, internal standards, and knowledge base content
- Workflow integration: establishing repeatable end-to-end processes from request to final deliverable, incorporating validation steps
- Prompt libraries and checklists: creating role-specific collections to enhance consistency and adoption
- Capstone exercise and 30-day adoption plan: converting a practical case for each participant into a repeatable workflow, focusing on quick wins and simple metrics
Requirements
This training targets professionals in engineering, technical, and operational fields who manage documentation, structured processes, data-informed decisions, and multi-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, with no prior advanced programming or data science background required. The curriculum is also applicable to operational and business support roles that regularly engage with technical information and require clearer, faster, and more uniform deliverables.
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 !
