How Reviews Are Scored
Quick Answer
TeamBench uses weighted criteria scoring. Each criterion you define receives an individual score from the AI model. These scores are then combined using the weights you assigned to produce an overall weighted quality score.
TeamBench uses a weighted scoring system that produces objective, repeatable quality scores for every piece of content. Understanding how scoring works helps you interpret results and configure your reviewers for maximum accuracy.
Every reviewer consists of multiple review criteria, each with a weight that reflects its importance. For example, a blog post reviewer might have criteria for readability (weight 30%), accuracy (weight 25%), SEO optimization (weight 20%), brand voice (weight 15%), and structure (weight 10%). The weights must add up to 100%.
When you submit content for review, the AI model evaluates each criterion independently. For every criterion, the model reads your guidance text, analyzes the content against that specific dimension, assigns a score on a 1-to-10 scale, and provides written feedback explaining the score. This per-criterion evaluation ensures each dimension receives focused attention.
The overall score is calculated as a weighted average. If your content scores 8 on readability (weight 30%), 6 on accuracy (weight 25%), 9 on SEO (weight 20%), 7 on brand voice (weight 15%), and 8 on structure (weight 10%), the overall score would be: (8 x 0.30) + (6 x 0.25) + (9 x 0.20) + (7 x 0.15) + (8 x 0.10) = 7.55 out of 10.
The individual criterion scores are where the real value lies. A high overall score with one low criterion score tells you exactly what to fix. A uniformly mediocre set of scores suggests the content needs broader rework. This granularity is what separates structured review from vague "looks good" feedback.
Score consistency is one of TeamBench's key advantages over manual review. The same content reviewed by the same reviewer will produce similar scores each time, because the evaluation criteria and guidance are fixed. This eliminates the variability that comes from different human reviewers having different standards on different days.
You can set quality thresholds on your reviewers to define what constitutes a passing score. For example, you might require an overall score of 7 or above and no individual criterion below 5. Content that meets the threshold can be published with confidence. Content that falls short gets specific, actionable feedback for improvement.
Over time, reviewing your score history reveals patterns. You might discover that your team consistently scores low on SEO optimization but high on readability. This data-driven insight allows you to focus training and resources where they will have the most impact on overall content quality.
Related Questions
What is a good review score?
Scores of 7 and above generally indicate solid content. Scores of 8-10 indicate high-quality content that meets professional standards. Scores below 5 suggest significant issues that need attention. The right threshold depends on your quality standards and content type.
Why do scores vary between AI models?
Different AI models have different evaluation styles. Some models tend to score more generously, while others are stricter. This is normal and does not mean one model is better. Pick a model and stick with it for consistency when comparing scores over time.
Can I change the scoring scale?
TeamBench uses a 1-to-10 scale for all criteria scores. This provides enough granularity to distinguish between good, great, and excellent content without being so fine-grained that scores become meaningless.
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