design visual communicationai uxconversational designhuman in the loopprompt patterns
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Portfolio
35% weight
Review shipped AI interfaces: chat assistants, copilots, recommendation surfaces. Ask what they designed for confidence display, citation, fallback states, and how usage or task success changed.
Evidence to listen for
Work exists and can be looked at, not just described
States what they made versus what the team or a template made
Shows range rather than one repeated style
Can walk through a piece from brief to final
Five-point scoring guide
1
Poor
No portfolio, or work that is unattributable or clearly templated.
2
Needs Improvement
Thin portfolio; unclear what they personally made.
3
Satisfactory
Real work with adequate range; contribution mostly clear.
4
Very Good
Strong varied portfolio with clear personal ownership.
5
Excellent
Shows live AI products with before and after task success or trust metrics, plus the flows they personally owned.
02
Evaluation factor
Craft and rationale
25% weight
Probe craft in probabilistic UX: error and hallucination states, streaming responses, prompt affordances, model latency handling, Figma prototypes wired to real or mocked LLM output.
Evidence to listen for
Explains why a layout, type choice, or colour decision serves the brief
Knows typography and hierarchy as craft, not decoration
Works to a brand system without either breaking it or hiding behind it
Names the tools they are genuinely fast in
Five-point scoring guide
1
Poor
Cannot explain any decision; work is arbitrary.
2
Needs Improvement
Talks in taste terms only; no link between choice and brief.
3
Satisfactory
Sound craft with some ability to justify decisions.
4
Very Good
Articulate about why each choice serves the brief.
5
Excellent
Explains design choices in terms of model behaviour, uncertainty, and user recovery paths rather than visual preference alone.
03
Evaluation factor
Feedback and iteration
25% weight
Assess how they tested AI features: wizard-of-Oz sessions, red-teaming prompts with users, reviewing conversation transcripts, and what they changed after seeing users mistrust or over-trust output.
Evidence to listen for
Takes critique without treating it as an attack
Distinguishes a subjective preference from a real problem, and says so politely
Iterates fast rather than defending version one
Delivers files correctly and on time
Five-point scoring guide
1
Poor
Defensive about critique; will not revise.
2
Needs Improvement
Accepts feedback passively; iterations do not improve the work.
3
Satisfactory
Revises willingly; struggles to push back on weak feedback.
4
Very Good
Iterates quickly and can argue for the work when the feedback is wrong.
5
Excellent
Cites specific transcript or usability findings that forced a redesign, including features they removed or gated.
04
Evaluation factor
Working with the brief
15% weight
Look for work with ML engineers and product on what the model can actually do: dataset limits, evaluation criteria, responsible AI or transparency guidelines they helped write.
Evidence to listen for
Asks about audience and goal before opening the design tool
Works with marketing, product, or clients rather than in isolation
Flags an impossible brief early
Hands over files and assets others can actually use
Five-point scoring guide
1
Poor
Designs in isolation; ignores the brief's purpose.
2
Needs Improvement
Starts designing before understanding the goal.
3
Satisfactory
Asks the right questions when prompted.
4
Very Good
Interrogates the brief up front and hands over cleanly.
5
Excellent
Translates model constraints into scoped design decisions and negotiates feasibility with engineers using shared evaluation language.
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Score every candidate against the same standard
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