Hello Recruiter gets smarter the more you use it — but small adjustments from your side can dramatically improve results.
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.
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)
What to Add to Your Knowledge Base
| Content Type | Why It Helps | Example |
|---|
| Company overview | AI can assess genuine interest in your company | Mission statement, founding story, market position |
| Culture & values | AI evaluates cultural fit accurately | ”We value ownership over consensus,” team rituals |
| Product details | AI can gauge technical understanding | What you build, who uses it, tech stack |
| Role-specific context | AI asks sharper questions | Team structure, current challenges, first 90-day goals |
| What “great” looks like | AI calibrates scoring | Description of your top performers in similar roles |
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?“).
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.
4. Monitor Key Metrics
Track these numbers monthly and look for trends:
| Metric | What It Tells You | Target |
|---|
| Interview completion rate | Whether candidates are dropping off mid-interview | > 70% |
| Average score distribution | Whether your criteria are well-calibrated | Bell curve, not clustered |
| Time-to-hire | How fast you’re moving from posting to offer | Decreasing over time |
| Risk signal frequency | Whether your job is attracting fraudulent applicants | < 10% of applicants |
| Offer acceptance rate | Whether your process is competitive | > 60% |
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.
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.
