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Hello Recruiter gets smarter the more you use it — but small adjustments from your side can dramatically improve results.

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1. Review and Refine Your Evaluation Criteria

After your first batch of candidates (aim for at least 10–15 completed interviews), review the scorecards and ask yourself:
  • Are high-scoring candidates actually good hires? If not, your criteria or weightages may need adjustment. A criterion weighted at 30% will dominate the overall score — make sure it deserves that weight.
  • Are strong candidates scoring low? The criteria may be too narrow, or you’re weighting “nice-to-have” skills as heavily as “must-have” skills. Move learnable skills to lower weightages.
  • Is there a criterion where everyone scores the same? If all candidates score 7/10 on “communication skills,” that criterion isn’t differentiating anyone. Make it more specific (e.g., “ability to explain technical concepts to non-technical stakeholders”) or reduce its weight.
  • Are follow-up questions revealing useful information? If the AI’s follow-ups aren’t surfacing new insights, your initial criteria may be too surface-level.
The sweet spot is 5–7 criteria. Fewer than 5 and you miss important dimensions. More than 7 and scores become diluted — every criterion contributes so little that the overall score becomes meaningless.

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2. Invest in Your AI Knowledge Base

The Knowledge Base is your single biggest lever for improving AI evaluation quality. The more context the AI has, the better it can:
  • Ask relevant, role-specific follow-up questions
  • Distinguish between generic answers and truly informed ones
  • Evaluate cultural fit based on your actual values (not generic ones)

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What to Add to Your Knowledge Base

Teams that maintain a rich Knowledge Base see meaningfully better scorecard accuracy. The AI moves from asking generic questions (“Tell me about your experience”) to specific ones (“How would you approach scaling a microservices architecture for 10x traffic growth?“).

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3. Optimize Your Job Descriptions

Your job description directly affects who applies — and low-quality applicant pools lead to low-quality hiring outcomes regardless of how good your AI evaluation is.
  • Study your best-performing jobs. Which postings attracted the most qualified applicants? What did those descriptions have in common? Replicate what works.
  • Use the AI Job Description generator. It optimizes for inclusive language and search visibility, which broadens your applicant pool.
  • Be honest about requirements. Listing 15 “required” skills when only 5 actually matter will scare away qualified candidates and attract overconfident ones.

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4. Monitor Key Metrics

Track these numbers monthly and look for trends:
If your average scores cluster at the extremes (everyone is 90+ or everyone is below 40), your criteria are miscalibrated. Scores should form a rough bell curve with clear differentiation in the middle range.

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5. Build a Review Habit

The highest-performing teams on Hello Recruiter treat their hiring process like a product — they iterate on it regularly. Monthly review checklist:
  • Review scorecards from the past month — do scores correlate with hiring decisions?
  • Check completion rates — are candidates dropping off?
  • Update the Knowledge Base with anything new (new product launches, team changes, updated tech stack)
  • Refine one evaluation criterion based on what you’ve learned
  • Test your own interview if you’ve made changes
30 minutes a month is all it takes. Small, consistent improvements compound into significantly better hiring outcomes over a quarter.

Evaluation Criteria

AI Knowledge Base

Interview Design