Design Interview questions

AI Experience / Conversational Designer interview questions

These Conversational Designer interview questions come from our vetting, where 3.4% of applicants pass 5 gates. You get 20 questions with answer hints, a task, red flags and a scorecard.

Updated: October 2026Remote Pro Ai · Design20 questions

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How we assess

Every applicant goes through 5 gates: application, an AI-assisted screen, a live technical assessment scored by a practice lead, a client-fit interview, and reference and credential checks. The questions below come from the live assessment. We score answers on reasoning and trade-offs, not on recalling definitions.

20 technical questions with model-answer hints

Questions 1–⁠10 (role-specific)

  1. How is designing for an LLM different from designing a rule-based chatbot?

    Model-answer hint

    Probabilistic outputs, guardrails instead of scripts, evaluation, prompts as design material.

  2. How should an assistant respond when it is unsure?

    Model-answer hint

    Signals uncertainty, asks a clarifying question, offers sources or a human.

  3. Design a fallback strategy for a banking assistant.

    Model-answer hint

    Graceful clarification, retry limits, escalation to an agent with full context.

  4. What belongs in a system-prompt guideline?

    Model-answer hint

    Role, scope, tone, refusal rules, formatting, safety constraints, examples.

  5. How do you evaluate a conversational experience?

    Model-answer hint

    Task success, groundedness, tone adherence, user trust ratings, failure taxonomy.

  6. When should an agent ask for confirmation?

    Model-answer hint

    Irreversible, financial or sensitive-data actions, balanced against friction.

  7. How do you handle Arabic-English code-switching?

    Model-answer hint

    Detects language, replies in the user’s language, tests mixed inputs and dialect samples.

  8. How do you show sources without overwhelming users?

    Model-answer hint

    Inline citations, expandable detail, only the most relevant sources.

  9. How do you work with AI engineers on prompt changes?

    Model-answer hint

    Versioned prompts, evaluation before release, joint trace reviews.

  10. Tell us about an AI interaction you redesigned after testing.

    Model-answer hint

    Evidence, the change made, measured effect.

Questions 11–⁠20 (Design core)

  1. How do you decide what to research before designing?

    Model-answer hint

    biggest unknowns and riskiest assumptions first; method matched to the question and the timeline.

  2. How do you turn research findings into design decisions?

    Model-answer hint

    synthesis into insights, prioritised opportunities, a traceable link from evidence to design.

  3. How do you design for users with low digital literacy?

    Model-answer hint

    plain language, familiar patterns, fewer steps, forgiving inputs, testing with those users.

  4. How do you design within strict regulatory or legal constraints?

    Model-answer hint

    involve compliance early, treat mandatory content as requirements, offer plain-language alternatives, record approvals.

  5. How do you include people with disabilities in research and testing?

    Model-answer hint

    recruitment through partner organisations, assistive-technology set-ups, accessible materials, fair incentives.

  6. How do you present design work to executives?

    Model-answer hint

    problem and goal first, options with trade-offs, evidence, a clear decision request.

  7. How do you use generative AI tools in your workflow responsibly?

    Model-answer hint

    exploration and speed, human refinement, licensing and brand rules, no confidential data in prompts.

  8. How do you design error states and edge cases?

    Model-answer hint

    list failure modes, give clear recovery guidance, preserve user input, test with real data.

  9. How do you keep quality high when deadlines are tight?

    Model-answer hint

    protect critical journeys, reuse system components, agree what is deferred, record design debt.

  10. How do you work with engineers while a feature is being built?

    Model-answer hint

    available for questions, reviews preview builds, pragmatic adjustments, shared ownership of quality.

Interviewer tip: Ask for a real example after every answer. Strong candidates describe what they built, what broke and what they changed.

Practical task

Timebox 3 hours. Design the conversation experience for a government services assistant that answers questions about permit renewals and can start a renewal. Deliver an intent map, 2 detailed flows including failure and handover paths, a 1-page system-prompt guideline and 10 evaluation test cases with expected behaviour.

How to run it Share the brief 24 hours ahead or run it live. Ask the candidate to talk through trade-offs as they work; reasoning matters more than a finished artefact.

Red flags our practice leads watch for

  • Designs only the happy path.
  • Treats prompts as an engineering detail outside design scope.
  • Has no method for measuring conversational quality.
  • Gives the assistant human traits that mislead users about what it is.
  • Universal flag: Vague answers that never reach a concrete system, number or decision the candidate personally owned.

The scorecard we use

Score each dimension 1–⁠4 (1 = no evidence, 2 = partial, 3 = solid, 4 = exceptional):

DimensionWeightWhat a 4 looks like
Technical depth, production judgement and security40%Explains how and why, designs for failure and sensitive data, and knows each tool’s limits
Practical task and problem solving30%Delivers a working solution with tests and documented decisions
Communication and documentation20%Explains clearly to technical and non-technical audiences, in English or Arabic
References and regional context10%References confirm the claimed scope; knows the frameworks and data-residency rules that apply

Pass bar: weighted average of 3.0 out of 4 or higher, with no dimension below 2. The practice lead approves the final result.

Skip the interview loop

Every specialist on our shortlist has already passed this assessment. Working in 4 working days.

Frequently asked questions

Yes. The questions, hints, practical task and scorecard are free to use. They work best when every interviewer scores the same dimensions independently and compares notes afterwards. Ask for a concrete example after each answer, because recalled definitions say little about how someone performs on a live system.

They come from the same live technical assessment, but practice leads rotate and adapt questions so candidates cannot rehearse them. What stays fixed is the scope being tested, the practical task format and the scorecard, which is why the published version is safe to share.

Plan for a technical conversation covering 8 to 10 of the questions, plus the practical task either live or as a short take-home. Use the remaining questions as follow-ups. If you would rather skip the loop, Remote Pro Ai shortlists already-assessed specialists within 48 hours.

Our pass bar is a weighted average of 3.0 out of 4 or higher across the 4 dimensions, with no dimension below 2. Production judgement and security, scored inside the 40% technical dimension, often separate strong candidates from those who interview well but struggle once they own a live system.

The task mirrors the first week of real work in this role and never uses client data. It runs live or as a short take-home of a few hours, and the candidate then explains the trade-offs behind each decision. Reasoning and production habits count for more than a polished final artefact.