Compare outputs between AI model iterations with direct, head-to-head preference testing to validate statistically significant improvements between versions, and to determine user preferences between models.
We offer direct, head-to-head preference testing that occurs when comparing two different speech model versions to validate statistically significant user preference for one over the other.
- This way of agreeing, creating, and analyzing different model versions eliminates language issues leading to better final version outcomes.
- The process of ABx testing model versions is straightforward and the outcomes are transparent.
- Randomized presentation of AB pairs (A -> B vs B -> A) across users reduces ordering bias
Check our API Documentation to know more.