My work covers engineering automation, Python, and technical evaluation.
I focus on results that a reviewer can trace to their inputs. This includes explicit units, repeatable calculations, data extraction that supports review, and useful information when a process fails.
Engineering AI Evaluation Portfolio
The portfolio contains four engineering case studies and five synthetic evaluation exercises. It includes scoring rules and Python tests that you can run.
- Check an answer about pressure references.
- Read the engineering case studies.
- Read the reference code.
- Read the tests.
- Check the validation evidence and limits.
- Find a technical term.
My interests include engineering AI evaluation, Python code review, technical task design, scoring rules, and quality assurance.
AI helped create the practice samples. The examples use synthetic data. Their candidate answers are authored illustrations, not recorded AI outputs. The scores do not measure model performance.