Engineering AI training data and evaluation

Specify the engineering context

Define the discipline, task and expected output. Mechanical design reasoning and electrical analysis need different expertise and review criteria.

• Calculation review: units, assumptions, intermediate steps and final results.
• Technical document interpretation: answers supported by the supplied specification.
• Design comparisons: tradeoffs evaluated against stated constraints.
• Troubleshooting: distinguish observations, hypotheses and missing evidence.

Make failure modes explicit

Build the rubric around errors that matter for the intended workflow. State where the model should decline to conclude or request additional information.

• Dimensional consistency and unit conversion.
• Boundary conditions and omitted assumptions.
• Use of the correct revision of a supplied document.
• Recognition of uncertainty and unsupported conclusions.

Require traceable review

Reference answers should explain the method and assumptions. Preserve reviewer feedback so the team can distinguish a model error from an ambiguous task.

• Task instructions and permitted reference material.
• Expected answer or acceptable solution range.
• Explanation of the method and review criteria.
• Disagreement resolution and known limitations.

Start with a scoped evaluation brief

Describe the workflow, technical discipline, current failure cases and target output format. Agree on the work and acceptance criteria before expanding it.

• Identify the specialty and experience needed.
• State which documents your team is authorized to provide.
• Keep held-out evaluation tasks separate from training examples.
• Decide how results will inform the next development step.

Common questions