Course Outline
Day 1: Foundations and Reliable Use of GenAI
AI and Generative AI essentials: understanding core concepts, functionality, value addition, and limitations
Practical prompting: utilizing reusable prompt structures, defining clear inputs, setting constraints, and specifying output formats
Iteration techniques: refining outputs through feedback loops and structured instructions
Output quality and verification: using checklists, cross-checking methods, identifying assumptions, ensuring traceability, and meeting acceptance criteria
Standardizing deliverables: creating templates for technical notes, summaries, reports, and action items
Documentation and requirements management: drafting, rewriting, structuring, summarizing, and writing change/requirement documents
Responsible use and data security: maintaining confidentiality, protecting IP, applying governance principles, and adhering to safety rules
Hands-on practice with realistic, anonymized scenarios
Day 2: Applied Use Cases, Productivity, and Workflow Integration
Analysis and reporting: transforming raw inputs into structured insights and executive-ready summaries
Problem solving and troubleshooting: leveraging AI for root cause analysis and action planning
Cross-functional communication: enhancing decision clarity, streamlining handovers, managing meeting minutes, and aligning stakeholders
AI as a copilot for code and automation: safely generating and reviewing code snippets, pseudocode, and test logic
Knowledge work acceleration: developing reusable procedures, internal standards, and knowledge-base content
Workflow integration: establishing repeatable end-to-end processes from request to deliverable, including validation steps
Prompt libraries and checklists: compiling role-based resources to improve consistency and adoption
Capstone practice and 30-day adoption plan: converting one practical case per participant into a repeatable workflow, focusing on quick wins and simple measurement metrics
Requirements
Designed for professionals in engineering, technical, and operational fields who manage documentation, structured processes, data-driven decisions, and cross-team collaboration. It is ideal for specialists and team leads seeking to boost productivity and output quality through Generative AI in routine tasks, requiring no advanced programming or data science background. The course also benefits operational and business support roles that frequently engage with technical information and require clearer, faster, and more 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 !
