Course Outline
Foundations and Reliable Use of GenAI
- Core AI and GenAI concepts: understanding definitions, mechanisms, value addition, and limitations
- Effective prompting: reusable prompt frameworks, precise inputs, constraints, and output specifications
- Iterative improvement: refining outputs through feedback loops and structured guidance
- Output quality and verification: utilizing checklists, cross-referencing, identifying assumptions, ensuring traceability, and meeting acceptance criteria
- Standardizing outputs: templates for technical notes, summaries, reports, and action items
- Documentation and requirements: drafting, rewriting, structuring, summarizing, and managing change/requirement specifications
- Responsible usage and data security: maintaining confidentiality, protecting IP, applying governance principles, and adhering to safe-use protocols
- Practical 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: utilizing AI for root cause analysis and developing action plans
- Cross-functional communication: clarifying decisions, managing handovers, recording meeting minutes, and aligning stakeholders
- AI as a coding and automation partner: 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 repeatable end-to-end processes from request to deliverable, including validation stages
- Prompt libraries and checklists: role-specific collections to enhance consistency and adoption
- Capstone exercise and 30-day adoption plan: converting one practical case per participant into a repeatable workflow, focusing on immediate wins and simple metrics
Requirements
This training is tailored for professionals operating in engineering, technical, and operational contexts who are responsible for documentation, structured processes, data-informed decision-making, and cross-team collaboration. It is ideal for specialists and team leaders aiming to boost productivity and output quality by leveraging Generative AI in routine tasks, with no prerequisite for advanced programming or data science expertise. The course is also pertinent for operational or business support positions that frequently engage with technical information and require clearer, more rapid, 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 !
