Course Outline
Foundations and Responsible GenAI Usage
- Core concepts of AI and GenAI: understanding capabilities, mechanisms, value propositions, and limitations
- Effective prompting strategies: developing reusable structures, defining clear inputs, setting constraints, and specifying output formats
- Iterative refinement: optimizing results through feedback loops and precise instructions
- Ensuring output integrity: utilizing checklists, cross-verification, identifying assumptions, ensuring traceability, and defining acceptance criteria
- Standardizing outputs: creating templates for technical notes, summaries, reports, and action items
- Managing documentation and requirements: drafting, rewriting, structuring, summarizing, and writing change or requirement specifications
- Ethical use and data protection: addressing confidentiality, IP safeguards, governance standards, and safe usage protocols
- Practical exercises using realistic, anonymized scenarios
Applied Use Cases, Efficiency, and Workflow Integration
- Data 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
- Enhancing cross-functional communication: improving decision clarity, handovers, meeting minutes, and stakeholder alignment
- AI as a coding copilot: safely generating and reviewing code snippets, pseudocode, and test logic
- Accelerating knowledge work: creating reusable procedures, internal standards, and knowledge base materials
- Workflow integration: establishing consistent end-to-end processes from request to deliverable, including validation checkpoints
- Building prompt libraries and checklists: developing role-specific resources to enhance consistency and adoption
- Capstone project and 30-day implementation plan: converting a personal practical case 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 multi-team collaboration, this training is ideal for specialists and team leads seeking to boost efficiency and output quality through Generative AI in daily tasks. No advanced programming or data science background is necessary. It is equally beneficial for operational or business support roles that engage frequently 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 !
