A/B Testing

A/B testing means trying out two different options in the same situation at the same time, and comparing the actual numbers to see which one works better. For example, a cafe might make two versions of a new menu recipe, serve version A to half the customers and version B to the other half, and see which one sells better. The key is making the decision based on real reactions, not gut feeling or personal taste.

The same principle applies to AI services. For instance, two versions of an AI model that answer the same questions might be prepared, with some users assigned the existing model and others the new model, then metrics like response speed, accuracy, and cost are compared. This lets teams check the performance difference before rolling the new model out to the entire service, without real risk.

As environments where you can choose among multiple AI models become more common, A/B testing is frequently used as a procedure to experimentally verify which model is better suited for a particular task.

The article introduces a scaling approach where, after deploying Fireworks AI open models integrated into Microsoft Foundry, the next step after traffic caching and performance tuning is to "A/B test multiple model variants." Here, A/B testing isn't a lab concept — it refers to an operational procedure of routing a portion of real service traffic to a new model version to compare cost, speed, and accuracy. A common misunderstanding is that A/B testing is not a process for training a model, but rather a validation step for choosing among already-built candidates.

Example prompt: Compare the two answers below and tell me which one is clearer and more useful, with reasons. Answer A: [paste first result] Answer B: [paste second result]

In an actual service, try asking the same question while switching between two AI models or prompts, then organize the answer quality, speed, and cost into a table to get a feel for A/B testing.