Karat Interview Results provide hiring teams with the big takeaways from the interview — including how the candidate performed relative to your bar, how the candidate performed in key assessment areas, and how the candidate performed relative to other candidates.
Let’s take a look at each of the main components that make up the results:
Top section of the Interview Results page
1. The Interview Outcome
The interview outcome provides an instant snapshot of the candidate’s performance. It is the main takeaway of the page and is determined by where the candidate’s Overall Score landed relative to your calibrated hiring bar(s).
For each role, hiring teams can define 2-4 Interview Outcomes. If you’re using Karat’s preset categories, the Interview Outcomes will be:
- Do Not Pursue (DNP)
- Requires Further Review (RFR)
- Invite to Next Round (INTR)
- Fast Track (FT)
Note: Karat is committed to upholding interview integrity. If an interview is flagged for suspicious behavior, it will be flagged as a fifth category Outcome Withheld - Integrity Risk, and an Interview Integrity Review banner will appear at the top of the results page. Learn more here.
2. The Overall Score
This “number score” is the total points that the candidate has earned throughout the interview. It is the sum of the individual scores that were received across the various assessment areas. On the graphic, you can see where the candidate’s score landed relative to your bar(s). It’s simple, the higher someone scores, the stronger their performance.
3. Performance Benchmarking
Using the Overall Score, we can compare the candidate’s performance to other candidates who you've interviewed for this same role. On the module, you'll see the candidate's performance percentile, lines indicating your hiring bar(s), and a gray shadow representing the distribution of all candidate scores.
Benchmarking is a great way to understand the relative quality of a candidate — providing insight into the strength of a candidate compared to your sourcing pool.
Middle section of the Interview Results page
4. Reviewing Interview Outcomes
Directly below the Performance Overview section, you’ll see the prompt: Is this candidate’s performance best categorized as [insert outcome]?” By selecting “Yes or No” and completing a brief feedback module, you’re telling us whether or not you agree with the outcome that was awarded. Your input here is invaluable in helping us improve overall alignment. Your feedback is critical for fine-tuning the hiring bars. Please provide as many details as possible. Submitting this review will not impact the candidate’s status or interview outcome.
For more information about reviewing Interview Outcomes.
5. Interview Summary
Next up, you’ll find the Interview Summary. This AI generated recap provides a high level overview of the interview and the candidate’s performance plus context into how the outcome was determined.
6. Scoring Breakdown
Moving on, you’ll find the Scoring Breakdown section. In this module, we explore how the candidate performed in each of the Interview’s assessment areas. The individual scores in this section are added together to determine the overall score.
Scanning left to right, you’ll see 4 columns:
- Areas Assessed: what was covered in the interview segment
- Allocated Score: the candidate’s score for that assessment area mapped over the maximum potential score and the minimum required score.
- Allocation: the weighting or indication of how much that area contributes to the overall score
- Percentile: how the candidate’s performance compared to your other candidates and all candidates across Karat
Expanded skills insights
7. Skills Insights
The Skills Insights section provides a deeper look into how the candidate performed across the specific skills evaluated during the interview.
Each skill is rated on a scale from Poor to Excellent, helping hiring teams quickly understand where the candidate demonstrated strong capabilities and where there may be opportunities for improvement.
Skills are grouped by competency areas, each representing a different aspect of engineering performance during the interview:
Explanations and Justifications
- Identifying & Explaining Code: Evaluates their comprehension of the existing codebase. We look for their ability to accurately describe what a function does, how data flows, and how different components interact.
- Justifying Choices: Measures their engineering rationale and their ability to defend their implementation. A strong candidate provides justifications grounded in solid engineering principles like maintainability, efficiency, or scalability.
AI Proficiency
- AI Productivity: Assesses how effectively the Al assistant is leveraged to increase productivity, prompting quality, and awareness of Al limitations. Vague or unhelpful requests demonstrate a weaker skill in this area.
- Evaluating AI Code: This is a critical Al skill, measuring if they blindly trust the Al's output. A top candidate treats Al code as a suggestion that must be verified and tested for correctness, style, and potential bugs.
Productivity
- Navigating Codebase: Assesses the ability to navigate the codebase without interviewer assistance. A strong candidate uses tools like file search or AI to quickly understand the project’s structure and pinpoint where to work.
- Coding Productivity: Measures raw output and effectiveness in completing the coding tasks. We look at whether they completed all required code, and if they had time for any follow-up enhancements, within the allotted time.
Product Sense & Problem Solving
- Debugging and Troubleshooting: Evaluates their process and reaction when their code doesn't work as expected. We look for a methodical, logical approach to identifying and fixing bugs, rather than random trial and error.
- Identifying Test Cases: Measures the testing mindset and their ability to think about potential failure points. A strong candidate will proactively identify and suggest test cases, such as those for edge cases or empty data sets.
- Clarifying Ambiguity: Evaluates recognizing that a problem is ambiguous and resolving that ambiguity before acting. This is a crucial skill for working with Al, which could often take a flawed or vague prompt at face value and get into solutioning.
Code Quality
- Code Efficiency and Design: Evaluates the elegance, performance, and maintainability of the solution. We check if the code is unnecessarily complex or if it uses appropriate data structures and algorithms for a clean design.
- Code Correctness: Assesses if the solution is bug-free and works correctly under all expected conditions. A low score here typically indicates a significant logical error in the implementation, not just minor syntax issues.
Together, these insights help answer the question: “What technical behaviors did the candidate demonstrate during the interview?” Hiring teams can expand each skill to view more detailed information about the evaluation.
8. Assessment
When you expand a skill, you’ll see an Assessment.
The Assessment provides a summary of the candidate’s performance for that specific skill. This explanation summarizes what the candidate did during the interview and why they received the rating shown on the skill scale.
These summaries are generated from the interviewer’s observations and provide additional context into:
- how the candidate approached the problem
- how they explained their reasoning
- how they interacted with the codebase and AI tools
The Assessment helps hiring teams quickly understand why a candidate received a particular skill rating.
9. Evidence
Directly below the Assessment, you’ll find the Evidence section.
This section includes Evidence-Based Timestamps, a feature that links the moments when a candidate demonstrated key skills directly to the interview recording.
Each evidence entry includes:
- a short description of what occurred during the interview relevant to the skill being assessed
- a clickable timestamp that links directly to that moment in the interview recording
These timestamps allow you to quickly review the exact point in the interview where the candidate demonstrated a particular skill, providing greater transparency into how the evaluation was determined.
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Final section of the Interview Results page
10. Supplementary Insights
The last module provides hiring teams with an extra data point that works to provide a bit more context but, ultimately, does not factor into the Interview Outcome. Let’s look at both:
Communication
The candidate’s ability to communicate clearly and effectively as determined by:
- Clarity of Speech: How intelligible were the candidate’s spoken words?
- Quality of Explanation: How well did the candidate explain their thoughts?
- Comprehension: How well did the candidate understand the interviewer?
- Interaction: How professional was the candidate’s interaction with the interviewer?
Putting It All Together
The NextGen Interview Results page is designed to give hiring teams both a high-level recommendation and detailed insight into a candidate’s performance.
By reviewing the Interview Outcome, Overall Score, Skill Insights, and supporting Evidence, hiring teams can quickly understand how a candidate performed, how their skills were evaluated, and where those skills were demonstrated during the interview.
These insights help hiring teams make more informed and confident hiring decisions.