> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hellorecruiter.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Improving Hiring Results — Data-Driven Tips for Hello Recruiter
> Refine evaluation criteria, enrich your AI knowledge base, and use analytics to continuously improve your AI-powered hiring outcomes.
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.
## Related Guides