> ## Documentation Index
> Fetch the complete documentation index at: https://docs.alex.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Reviewing Candidate Matches

After you search for candidates or run a match against a job, the system provides a list of candidates with scores and AI-powered insights. These features are designed to help you quickly understand how well a candidate aligns with your role and identify the best people to move forward.

## Understanding Scores

Each candidate is given a score to represent their fit for the role. These scores help you prioritize your review process by highlighting the most promising candidates first.

### Interpret Match Score and Tiers

* **Match Score:** Each candidate receives a Match Score from 0 to 100. This score is a quick indicator of how well their profile matches the job criteria. The scores are color-coded for easy scanning:

  * **80 and above:** High match (Green)
  * **60 - 79:** Medium match (Yellow)
  * **Below 60:** Low match (Red)

  You can click on a candidate's score to open a detailed **Match Analysis** window. This view provides a breakdown of the score across several categories, including:

  * Title
  * Location
  * Salary
  * Education
  * Experience
  * Skills
  * Keywords
  * Industries

* **Match Labels:** In some views, you may see a simple label like "Strong match" or "Good match" instead of a numerical score. These labels serve as a quick visual guide to a candidate's fit.

* **Tiers:** Candidates are grouped into tiers. This helps organize the matched candidates to further assist in prioritizing your review.

## AI Insights

The system uses AI to provide detailed explanations for why a candidate is a good match, saving you time in your review.

### Review "Why They're a Good Fit" Reasoning

Each candidate card includes a section titled **"Why they're a good fit,"** which provides a brief, 2-3 sentence summary of their qualifications and how they relate to the job description.

For a more in-depth look, click on the candidate's **Match Score** to open the **Match Analysis** window. Here you will find:

* **Overall Match Reasoning:** A comprehensive summary explaining the candidate's overall fit.
* **Match Strengths:** A bulleted list of the candidate's key qualifications that align with the job requirements.
* **Discussion Points:** A list of areas that may require more clarification during an interview.
* **Criteria Breakdown:** A table that shows the individual score and reasoning for each matching category, such as skills, experience, and education.

## Candidate Profile Details

Key information from a candidate's profile is displayed directly on their match card, allowing you to get a quick overview without leaving the page.

### View Resumes, Contact Info, and Current Role

On each candidate card, you can find the following details:

* **Contact Information:** The candidate's email and phone number are displayed with icons. You can click the mail or phone icon to copy the information to your clipboard.
* **Resume:** Click the **Resume** button to open a pop-up window that displays the candidate's full resume.
* **Current Role and Other Details:** A details section on the card summarizes other important information, including:
  * Current Role
  * Years of Experience
  * Education
  * Skills

## Best Practices

* **Interpreting Match Scores**:
  * **80-100**: Excellent fit, high priority for outreach.
  * **60-79**: Good fit, worth reviewing.
  * **40-59**: Moderate fit, may have gaps.
  * **Below 40**: Weak fit, likely missing key requirements.
  * Always read the match reasoning to understand why a candidate scored the way they did. A lower score doesn't mean the candidate is unqualified -- gaps can be great discussion points for a screening call.
* **Writing Effective Search Queries**: Be specific but use natural language. Include key skills, titles, locations, and experience levels. For example: "Senior backend engineer in New York with experience in Go and AWS, at least 5 years of experience."
* **Optimizing Match Settings**:
  * For niche roles, consider a wider location radius or less strict recency filter.
  * Use status exclusions to filter out unavailable candidates (e.g., "Placed," "Do Not Contact").
  * For roles with common title variations, use "normal" title matching. Use "strict" only when the title is very specific.
* **Managing Auto-Invitation**:
  * Start with a higher minimum score threshold (e.g., 85) and gradually lower it if you need more candidates.
  * Set a reasonable cooldown period (e.g., 30 days) to maintain a positive candidate experience.