Why Knockout Questions Are the Most Underrated Hiring Tool

Most recruitment AI tools charge you for every candidate that enters the system. Upload 200 CVs? That's 200 credits used — even if 150 of those candidates don't meet your basic requirements.

We think that's backwards.

The problem with “analyze everything”

When every candidate gets full AI analysis, you're paying for noise. A junior developer applying to a senior role. A candidate who can't work in your timezone. Someone who doesn't have the required certification. These are obvious NOs that don't need AI to identify.

The result? Bloated costs, slower processing, and AI rankings polluted by candidates who should never have been in the pool.

Knockout questions: the pre-filter

Knockout questions are simple yes/no or multiple-choice questions that candidates answer during application. They test for non-negotiable requirements:

• “Do you have 5+ years of experience in product design?”
• “Are you authorized to work in the EU?”
• “Are you proficient in Figma?”
• “Can you start within 30 days?”

If a candidate fails any knockout question, they're automatically disqualified. No credit is used. No AI processing happens. They're simply marked as disqualified in your pipeline.

The math speaks for itself

In our testing, knockout questions typically filter out 30-50% of applicants before AI analysis begins. On a 100-candidate job posting, that's 30-50 credits saved — credits you can use on candidates who actually have a chance.

This means your AI analysis pool is cleaner, your rankings are more meaningful, and your cost per quality hire drops significantly.

How to write good knockout questions

The best knockout questions are binary and non-negotiable. If the answer could be “it depends,” it's probably better as a screening question (which we also support) rather than a knockout.

Focus on hard requirements: certifications, years of experience, location/timezone, language fluency, and legal work authorization. Save subjective qualities for AI analysis.