Evaluate AI Content Specialist 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 check which AI-assisted content actually published: blog clusters, product pages, newsletters, video scripts. Ask for traffic, ranking or conversion numbers. Use the rubric to compare role-specific evidence consistently.
For campaigns that performed, look for evidence the candidate names specific published pieces with before and after organic traffic or conversion figures, and states which parts were model-generated versus human-rewritten. For measurement and testing, look for evidence the candidate cites specific tests with sample sizes and outcomes, plus a fact-checking and citation workflow that caught real model errors before publication.
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.
01
Evaluation factor
Campaigns that performed
30% weight
Check which AI-assisted content actually published: blog clusters, product pages, newsletters, video scripts. Ask for traffic, ranking or conversion numbers and the tools used (ChatGPT, Claude, Jasper, Surfer).
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
1
Poor
No numbers; describes activity rather than results.
2
Needs Improvement
Vanity metrics only; attribution to their own work is unclear.
3
Satisfactory
Real campaigns with some numbers; baselines often missing.
4
Very Good
Named campaigns with metric, baseline, and clear personal ownership.
5
Excellent
Names specific published pieces with before and after organic traffic or conversion figures, and states which parts were model-generated versus human-rewritten.
02
Evaluation factor
Audience and segmentation
25% weight
Probe how they adapt tone for distinct segments and channels: technical buyers versus consumers, LinkedIn versus lifecycle email. Ask how brand voice guidelines get encoded into prompts or style files.
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
1
Poor
One message for everyone; no segmentation thinking.
2
Needs Improvement
Segments by demographics only; no behavioural insight.
3
Satisfactory
Workable segmentation; lifecycle logic is basic.
4
Very Good
Behaviour-driven segmentation and lifecycle design with evidence behind it.
5
Excellent
Describes maintaining a documented brand voice system, custom instructions or fine-tuned templates, with examples of rewriting one asset for different segments.
03
Evaluation factor
Measurement and testing
30% weight
Test their approach to headline and format testing, plagiarism and hallucination checks, and metrics they own: dwell time, indexed pages, assisted conversions in GA4 or HubSpot.
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
1
Poor
No testing; claims credit for correlation.
2
Needs Improvement
Runs tests but reads them badly; overclaims attribution.
3
Satisfactory
Tests sensibly; rigour drops under deadline pressure.
4
Very Good
Disciplined testing with honest attribution and a result that surprised them.
5
Excellent
Cites specific tests with sample sizes and outcomes, plus a fact-checking and citation workflow that caught real model errors before publication.
04
Evaluation factor
Working with the business
15% weight
Assess how they work with SEO, product, legal and subject matter experts: brief intake, review cycles, AI disclosure policy, and handling editors who distrust generated drafts.
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
1
Poor
Works in isolation; reports only flattering numbers.
2
Needs Improvement
Limited coordination; briefs are thin.
3
Satisfactory
Coordinates adequately; reporting is honest if basic.
4
Very Good
Strong partner to sales and product; briefs and reports clearly.
5
Excellent
Shows a repeatable editorial workflow with named collaborators, SME interview habits, and a clear position on AI disclosure and content governance.
Evidence-led prompts
Interview questions for a AI Content Specialist
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
Can you share examples of content strategies you developed using these tools?
02
Can you give an example where these tools improved content quality or efficiency?
03
How have you used these tools in previous content roles?
04
What is your understanding of how these tools work for content?
