Course Outline
Foundations and Reliable Use of GenAI
- AI and GenAI essentials: definitions, mechanics, value-add areas, and limitations
- Practical prompting: reusable prompt structures, clear inputs, constraints, and output formats
- Iteration techniques: refining results through feedback loops and structured instructions
- Output quality and verification: checklists, cross-checking, assumptions, traceability, acceptance criteria
- Standardizing deliverables: templates for technical notes, summaries, reports, and action items
- Documentation and requirements: drafting, rewriting, structuring, summarizing, and change/requirement writing
- Responsible use and data security: confidentiality, IP protection, governance principles, and safe-use rules
- Hands-on practice with realistic, anonymized scenarios
Applied Use Cases, Productivity, and Workflow Integration
- Analysis and reporting: converting raw inputs into structured insights and executive-ready summaries
- Problem solving and troubleshooting: AI-supported root cause analysis and action planning
- Cross-functional communication: decision clarity, handovers, meeting minutes, and stakeholder alignment
- AI as a copilot for code and automation: safe generation and review of snippets, pseudocode, and test logic
- Knowledge work acceleration: building reusable procedures, internal standards, and knowledge-base content
- Workflow integration: repeatable end-to-end processes from request to deliverable, with validation steps
- Prompt libraries and checklists: role-based collections to improve consistency and adoption
- Capstone practice and 30-day adoption plan: one practical case per participant turned into a repeatable workflow, with quick wins and simple measurement
Requirements
This training is tailored for professionals in engineering, technical, and operational settings who engage with documentation, structured processes, data-informed decisions, and cross-team collaboration. It is ideal for specialists and team leads seeking to enhance productivity and output quality by integrating Generative AI into their routine duties, without the need for advanced programming or data science expertise. Additionally, the course is beneficial for operational or business support functions that frequently deal 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 !
