Course Outline
Foundations and Reliable Use of GenAI
- Core AI and GenAI concepts: understanding mechanisms, identifying value-add areas, and recognizing limitations
- Practical prompting: utilizing reusable prompt structures, defining clear inputs, constraints, and desired output formats
- Iterative refinement: optimizing results through feedback loops and structured instructional techniques
- Ensuring output quality: applying checklists, cross-verification, assumption testing, traceability, and acceptance criteria
- Standardizing outputs: developing templates for technical notes, executive summaries, reports, and action items
- Documentation mastery: techniques for drafting, rewriting, structuring, summarizing, and writing change/requirement specifications
- Responsible usage and data security: adhering to confidentiality standards, IP protection, governance principles, and safe-use protocols
- Practical exercises using realistic, anonymized business scenarios
Applied Use Cases, Productivity, and Workflow Integration
- Enhancing analysis and reporting: transforming raw data into structured insights and executive-ready summaries
- AI-assisted problem solving: leveraging AI for root cause analysis and strategic action planning
- Improving cross-functional communication: ensuring decision clarity, effective 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: creating reusable procedures, internal standards, and knowledge base content
- Workflow integration: establishing repeatable end-to-end processes from request to delivery, incorporating validation checkpoints
- Prompt libraries and checklists: curating role-based collections to enhance consistency and adoption rates
- Capstone project and 30-day adoption strategy: converting a practical case study into a repeatable workflow, identifying quick wins, and establishing simple metrics
Requirements
Designed for professionals in engineering, technical, and operational domains who manage documentation, structured processes, data-driven decision-making, and cross-team collaboration. This course is ideal for specialists and team leads seeking to enhance productivity and output quality through Generative AI in routine tasks, without requiring expertise in advanced programming or data science. It is equally beneficial for business support and operational roles that regularly engage with technical data and require precise, efficient, and consistent 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 !