Course Outline
Day 1: Foundations and Reliable Use of GenAI
AI and GenAI essentials: understanding its nature, mechanics, value proposition, and limitations
Practical prompting: utilizing reusable prompt structures, clear inputs, constraints, and defined output formats
Iteration techniques: refining outputs through feedback loops and structured instructions
Output quality and verification: employing checklists, cross-checking, identifying assumptions, ensuring traceability, and defining acceptance criteria
Standardizing deliverables: creating templates for technical notes, summaries, reports, and action items
Documentation and requirements: drafting, rewriting, structuring, summarizing, and writing change/requirement documents
Responsible use and data security: adhering to confidentiality, IP protection, governance principles, and safe-use protocols
Hands-on practice with realistic, anonymized scenarios
Day 2: Applied Use Cases, Productivity, and Workflow Integration
Analysis and reporting: converting raw data into structured insights and executive-ready summaries
Problem solving and troubleshooting: conducting AI-supported root cause analysis and action planning
Cross-functional communication: enhancing decision clarity, managing handovers, drafting meeting minutes, and aligning stakeholders
AI as a copilot for code and automation: safely generating and reviewing 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
Requirements
This training targets professionals operating in engineering, technical, and functional environments who manage documentation, structured processes, data-informed decision-making, and inter-team collaboration. It is ideal for specialists and team leaders seeking to boost productivity and output quality by integrating Generative AI into daily tasks, without the need for advanced programming or data science expertise. The course also benefits operational or business support roles that regularly 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 !
