Course Outline
Day 1: Foundations and Reliable Use of GenAI
Understanding AI and Generative AI: core concepts, functionality, value proposition, and limitations
Effective prompting: utilizing reusable prompt frameworks, defining clear inputs, applying constraints, and specifying output formats
Refinement techniques: enhancing results through iterative feedback loops and structured directives
Ensuring output quality and verification: employing checklists, cross-referencing, validating assumptions, maintaining traceability, and meeting acceptance criteria
Standardizing deliverables: developing templates for technical notes, summaries, reports, and action items
Documentation and requirements management: drafting, rewriting, structuring, summarizing, and crafting requirement documents
Ethical usage and data security: upholding confidentiality, protecting intellectual property, adhering to governance principles, and following safe-use guidelines
Practical exercises using realistic, anonymized case studies
Day 2: Applied Use Cases, Productivity, and Workflow Integration
Analysis and reporting: transforming raw data into structured insights and executive-ready summaries
Problem solving and troubleshooting: conducting AI-supported root cause analysis and action planning
Enhancing cross-functional communication: clarifying decisions, streamlining handovers, drafting meeting minutes, and aligning stakeholders
Leveraging AI as a coding copilot: safely generating and reviewing code snippets, pseudocode, and test logic
Accelerating knowledge work: creating reusable procedures, establishing internal standards, and building knowledge-base content
Integrating workflows: implementing repeatable end-to-end processes from request to deliverable, including validation checkpoints
Prompt libraries and checklists: curating role-specific resources to improve consistency and adoption rates
Capstone project and 30-day adoption plan: converting one practical case per participant into a repeatable workflow, identifying quick wins and establishing simple metrics for success
Requirements
This course targets professionals operating in engineering, technical, and business support 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 through Generative AI in routine tasks, with no advanced programming or data science background required. The training is also valuable for operational roles that regularly engage with technical content and aim to produce clearer, faster, and more consistent deliverables.
Testimonials (7)
The trainer's open and warm style, and the accessible language.
OPREAN ADELA - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
Adaptability and the fact that the instructor ensured that any confusion was explained and understood.
Rares Ursu - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
As I learned to correctly format a prompt
Alin Zoltan - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
The participant did not move to the next step until all learners had completed the task/exercise assigned. They explained and repeated the information whenever asked.
Drumia Radu - Gheorghe - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated
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 !
