Interview scorecard template

AI Content Creator interview scorecard

Evaluate AI Content Creator candidates across 4 weighted areas: campaigns that performed, measurement and testing, audience and segmentation, and working with the business. Campaigns that performed leads at 30%, so ask for specific AI-assisted content they shipped: blog series, short-form video, newsletters. Look for named tools (ChatGPT, Claude, Midjourney, ElevenLabs, Descript). Use the rubric to compare role-specific evidence consistently.

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TL;DR

For campaigns that performed, look for evidence the candidate names published pieces with organic sessions, watch time or signups, and explains which parts were AI-generated versus human rewritten. For measurement and testing, look for evidence the candidate cites concrete tests with before and after metrics, and describes killing or rewriting AI drafts that failed retention or ranking checks. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.

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.

Evaluation factor

Campaigns that performed

30% weight

Ask for specific AI-assisted content they shipped: blog series, short-form video, newsletters. Look for named tools (ChatGPT, Claude, Midjourney, ElevenLabs, Descript) and traffic or engagement numbers attached.

Evidence to listen for

Names campaigns or programmes they ran, with the metric they moved and the baseline it moved from
Distinguishes their own work from the team's or the agency's
Knows the audience and the offer, not just the channel
Can describe a campaign that failed and why

Five-point scoring guide

Poor

No numbers; describes activity rather than results.

Needs Improvement

Vanity metrics only; attribution to their own work is unclear.

Satisfactory

Real campaigns with some numbers; baselines often missing.

Very Good

Named campaigns with metric, baseline, and clear personal ownership.

Excellent

Names published pieces with organic sessions, watch time or signups, and explains which parts were AI-generated versus human rewritten.

Evaluation factor

Audience and segmentation

25% weight

Probe how they adapt tone and format per channel: LinkedIn thought leadership versus TikTok hooks versus lifecycle email. Check whether they build prompt libraries or brand voice guides per segment.

Evidence to listen for

Describes real customer segments and what differs between them, not demographic guesses
Has built lifecycle stages, journeys, or nurture logic that reflect actual behaviour
Knows where the audience data comes from and its limits
Writes for the segment rather than for everyone

Five-point scoring guide

Poor

One message for everyone; no segmentation thinking.

Needs Improvement

Segments by demographics only; no behavioural insight.

Satisfactory

Workable segmentation; lifecycle logic is basic.

Very Good

Behaviour-driven segmentation and lifecycle design with evidence behind it.

Excellent

Describes distinct voice and format choices for named personas, backed by a documented brand voice prompt or style guide they maintain.

Evaluation factor

Measurement and testing

30% weight

Test their testing: headline and thumbnail variants, hook A/B runs, GA4 or Search Console tracking, and how they detect AI content that underperforms or gets flagged.

Evidence to listen for

Tests deliberately rather than changing everything at once
Understands attribution limits and does not overclaim
Knows what sample size and duration a test needed
Can name a test whose result contradicted what they expected

Five-point scoring guide

Poor

No testing; claims credit for correlation.

Needs Improvement

Runs tests but reads them badly; overclaims attribution.

Satisfactory

Tests sensibly; rigour drops under deadline pressure.

Very Good

Disciplined testing with honest attribution and a result that surprised them.

Excellent

Cites concrete tests with before and after metrics, and describes killing or rewriting AI drafts that failed retention or ranking checks.

Evaluation factor

Working with the business

15% weight

Assess how they handle SME reviews, legal or factual accuracy checks, and disclosure policy on AI use. Look for evidence of working inside a content calendar with designers and product teams.

Evidence to listen for

Works with sales, product, or operations rather than throwing campaigns over a wall
Briefs designers and writers well enough to get usable work
Manages agencies or freelancers to a standard
Reports results to leadership without spin

Five-point scoring guide

Poor

Works in isolation; reports only flattering numbers.

Needs Improvement

Limited coordination; briefs are thin.

Satisfactory

Coordinates adequately; reporting is honest if basic.

Very Good

Strong partner to sales and product; briefs and reports clearly.

Excellent

Shows a fact-check and SME approval workflow, clear stance on AI disclosure, and steady delivery against an agreed publishing calendar.

Evidence-led prompts

Interview questions for a AI Content Creator

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

01
Can you give examples of campaigns where you used these tools to create content?
02
Describe a time when a tool did not deliver what you expected.
03
What limitations have you encountered, and how do you work around them?
04
What do you do to verify factual claims in generated content?
05
How do you ensure the originality of the content you publish?

See the complete AI Content Creator question set

Put this rubric to work

Score every candidate against the same standard

Add these weighted factors to Hirevire and let AI evaluate recorded answers against your rubric.

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