AI-assisted engineering and human review
Examine practical uses of artificial intelligence in engineering, including reference checking, data handling and accountable review.
Choose a bounded use case
Explain what the AI tool is being asked to do: summarise a document, draft code, classify information or help organise a review. State the expected output and how success will be evaluated. Keep an experiment separate from a decision that affects a real project.
Check claims against their sources
A useful discussion records which source documents were available, whether quotations or references could be verified and what happened when the system lacked information. Generated confidence is not evidence. Share the method used to detect unsupported statements and preserve the distinction between a retrieved source and a generated explanation.
Protect information and accountability
Describe the data permitted in the workflow, the review steps and who remains responsible for approval. Useful AI and machine learning conversations include reproducible evaluations, error examples, model-version changes and the practical cost of checking the output. Explain what the tool could not do as carefully as what it appeared to do well.
Questions to bring to the community
- How do you verify references in an AI-generated engineering summary?
- Which information must stay out of an external AI service?
- What does a useful evaluation set look like for your workflow?
