Best Practices

1. Test in the Playground First:
   • Use the A/B Test Playground to validate model behavior when starting an experiment, especially if the models are from different families or have significant architectural differences.
2. Choose Appropriate Feedback Types:
   • Use Dual Sentiment (Like/Dislike) for quick, binary feedback when simplicity is key.
   • Use Rating (1–5) for more granular feedback when nuanced performance differences are important.
3. Set Realistic Statistical Parameters:
   • Adjust Significance (α) and Statistical Power (1 - β) based on your tolerance for false positives and false negatives, respectively.
   • Use a smaller Minimum Detectable Effect Size (δ) if detecting subtle improvements is critical, but note that this increases the required sample size.
4. Monitor Experiments Regularly:
   • Check the A/B Test Results Page frequently to ensure the experiment is progressing as expected and to identify any anomalies early.

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Additional Resources

• Statistical Metrics Guide: Static Sig
• Blogpost on Model A/B Testing

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Next Steps

• Experiments – Launch and track structured experiments that complement A/B testing workflows.
• Runs – Dive into run‑level metrics and artifacts to pinpoint model behaviour variations uncovered during testing.
• Human Feedback Fundamentals – Master best practices for collecting and analysing real‑time user feedback.
• Inference REST API – Integrate your A/B test endpoint into external applications with secure, token‑based calls.