Introduction to A/B Testing
Learn how to compare variants and optimize your AI applications with A/B testing in Narev.
LLMs are black boxes. We already know how to optimize black boxes—it's through A/B testing.
A/B testing has been used to optimize everything from social media features at Facebook and LinkedIn, to e-commerce conversion rates, to product pricing strategies, and even political campaigns. It's the proven way to make data-driven decisions when you can't predict outcomes in advance.
The same principle applies to LLMs: compare different configurations on real prompts to see what works best.
How Narev A/B Testing Works
We make it easy to run A/B tests on your AI use cases.
- Define your A/B test - The A/B test receives prompts from your system
- Create variants - Different model configurations to test (different models, temperatures, prompts, or parameters)
- Run experiments - Narev tests all variants against your prompts and measures cost, latency, and quality
Each A/B test can run multiple experiments by creating different variants and tweaking their configurations.
What to test
Variants let you compare:
- Different models (e.g., GPT-4o Mini vs Claude-3.5 Haiku)
- Different providers of models (e.g., Claude from Anthropic vs Claude from AWS Bedrock)
- Different parameters
- Different system prompts
Narev automatically tracks cost, latency, and quality metrics to show you which variant performs best.