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# AI Scoring Limitations and Best Practices

# AI Scoring Limitations and Best Practices

Understand what AI scoring can and cannot evaluate to write better criteria and get more accurate results.

## What AI Scoring Can Assess

AI analyzes these data sources:

* Resume content
* Profile fields
* Questionnaire responses
* Evaluation form answers
* Email message content
* Discussion notes
* Job description ![AI Scoring Limitations and Best Practices: What AI Scoring Can Assess](https://storage.crisp.chat/users/helpdesk/website/-/4/f/a/a/4faab8f2b2ab0400/ai-scoring-limitations-best-pr_nxtiv6.png)

## What AI Scoring Cannot Assess

The AI can only evaluate information that exists in the data sources. It cannot assess:

* **Speaking accent or verbal communication** (unless from interview notes)
* **Personality traits** (unless documented in feedback)
* **Physical presence or demeanor**
* **Skills not mentioned in resume or profile**
* **Cultural fit** (unless specific behaviors are documented)

**Key principle:** If it's not written down, AI cannot score it.

## Premium Model vs Standard

### Standard Model
* Uses available profile data only
* Lower credit cost per candidate
* Good for high-volume initial screening

### Premium Model
* 2x smarter analysis
* Searches external information about candidates
* Finds company details: products, headcount, industry
* Costs ~10x more AI credits per run
* Best for senior or critical positions

## Writing Effective Criteria

### Be Specific

**Good:**
> "Evaluate B2B SaaS sales experience. Look for: ARR/MRR metrics, enterprise deal sizes ($50K+), multi-stakeholder sales cycles. Score 0-30 if SMB only, 40-70 if some enterprise experience, 80-100 if proven enterprise track record."

**Too vague:**
> "Good sales experience"

### Match Criteria to Available Data

Before writing criteria, consider what data you'll have:

| Hiring Stage | Available Data | Best Criteria |
| ---- | ---- | ---- |
| Application only | Resume, basic fields | Experience, education, skills listed |
| After questionnaire | Questionnaire answers | Specific qualifications, situational responses |
| After interview | Evaluation notes | Communication, problem-solving, culture fit |

### Use Scoring Guidelines

Include explicit scoring instructions:

> "Score 0-25 if no relevant experience, 26-50 if 1-2 years, 51-75 if 3-5 years with progression, 76-100 if 5+ years with leadership"

This helps AI apply consistent standards.

## Best Practices

### Start Simple
* Begin with 3-5 criteria
* Add more only if needed
* Focus on must-have requirements

### Weight Appropriately
* Use weights 1-10 for each criterion
* Higher weights = more impact on total score
* Don't make everything weight 10 ![AI Scoring Limitations and Best Practices: Weight Appropriately](https://storage.crisp.chat/users/helpdesk/website/-/4/f/a/a/4faab8f2b2ab0400/ai-scoring-limitations-best-pr_wqfh2j.png)

### Pre-Screen First
To save AI credits:
1. Use knockout questions to eliminate unqualified candidates
2. Only AI score candidates who pass basic requirements
3. This reduces credit usage significantly

### Review and Adjust
* Check AI scores against your own assessments ![AI Scoring Limitations and Best Practices: Review and Adjust](https://storage.crisp.chat/users/helpdesk/website/-/4/f/a/a/4faab8f2b2ab0400/ai-scoring-limitations-best-pr_15nsblb.png)
* If scores don't match expectations, refine criteria
* More specific prompts = better accuracy

## Common Mistakes


1. **Criteria too broad:** "Good communication" - instead specify what evidence to look for
2. **Assessing unavailable info:** "Speaking ability" from resume only
3. **Missing scoring guidelines:** No clear scale for what constitutes 50 vs 80
4. **Too many criteria:** 10+ criteria dilutes each one's impact
5. **All high weights:** If everything is weight 10, nothing is prioritized

## When to Use Each Model

### Use Standard Model For:
* High-volume roles
* Initial screening rounds
* Junior positions
* When profile data is sufficient

### Use Premium Model For:
* Executive searches
* Specialized technical roles
* When company background matters
* Final candidate comparisons