AI Dictionary › Fondamenti AI
A/B testing per sistemi AI
A/B testing for AI systems is an experimental method that compares two versions of a model or its configuration, showing them to different groups of real users and measuring which of the two produces better results on concrete indicators, such as satisfaction, task completion rate or response time. Unlike static benchmarks, it measures impact under real usage conditions.
A key indicator to improve is defined, users are randomly split into groups assigned to version A or version B of the system, and data on their behavior is collected over a defined period. Results from the two groups are then compared using statistical methods to determine whether the observed difference is significant or due to chance. Only then is a decision made on whether to roll out the new version to all users.
It is widely used to validate updates to conversational models, changes to system prompts, or new AI-based features before a full release. It allows decisions to be based on real data rather than scores obtained only in a controlled environment.
The method originates in applied statistics and digital marketing, where it has been used for decades to compare variants of web pages or campaigns; it was later adopted to evaluate AI-based systems in production as well.
Grace uses A/B testing to compare variants of its evaluation scenarios, checking which wording produces clearer data on users' skills.
From our network
INDACO TMS: Transport Management for European Logistics
Shipment tracking, multi-carrier EDI and automated invoicing in one cloud platform. Invoices generated in under 10 seconds.
Visit indacotms.com →From the Agora Intelligence blog
📱 Download the Android app (beta) iOS coming soon
Say what you mean. Get what you need.
Grace Certified, the AI coach that trains and certifies your prompt engineering, by Agora Intelligence.