Course Outline
Foundations and Reliable Use of GenAI
- Core concepts of AI and GenAI: understanding capabilities, mechanisms, value propositions, and limitations
- Effective prompting strategies: utilizing reusable structures, precise inputs, constraints, and defined output formats
- Iterative refinement: enhancing results through feedback loops and structured directives
- Quality assurance: implementing checklists, cross-verification, assumption tracking, traceability, and acceptance criteria
- Deliverable standardization: creating templates for technical notes, summaries, reports, and action items
- Documentation mastery: drafting, restructuring, summarizing, and writing change or requirement specifications
- Responsible AI adoption: ensuring confidentiality, IP protection, governance compliance, and safe usage protocols
- Practical exercises using realistic, anonymized scenarios
Applied Use Cases, Productivity, and Workflow Integration
- Analytical efficiency: transforming raw data into structured insights and executive-level summaries
- Problem resolution: leveraging AI for root cause analysis and strategic action planning
- Cross-functional communication: enhancing decision clarity, handovers, meeting minutes, and stakeholder alignment
- Code copilot functionality: safely generating and reviewing code snippets, pseudocode, and test logic
- Knowledge acceleration: developing reusable procedures, internal standards, and knowledge base content
- Workflow integration: establishing repeatable end-to-end processes with embedded validation steps
- Prompt library management: curating role-based collections to ensure consistency and widespread adoption
- Capstone project and 30-day adoption roadmap: converting a practical case study into a repeatable workflow, focusing on quick wins and simple metrics
Requirements
Tailored for professionals in engineering, technical, and operational domains who manage documentation, structured processes, data-informed decisions, and inter-team collaboration, this course is ideal for specialists and team leads seeking to elevate productivity and output quality through Generative AI. No advanced programming or data science background is required. Additionally, the content is highly relevant for operational and business support roles that frequently engage with technical data and require clearer, faster, and more consistent results.
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 !