How to test AI models

When developing AI features, selecting the right model for a specific use case is critical, as relying on benchmarks alone can yield inconsistent results due to their focus on standardized datasets rather than real inputs. Braintrust facilitates structured model testing by allowing developers to run multiple models on the same inputs and automatically scoring each output, which enables comparisons based on measurable outcomes rather than subjective preferences. The process involves creating a dataset of real user inputs, managing versioned prompts, and comparing models using a unified API that supports various providers. This methodology helps address key factors like output quality, cost, and latency, ensuring that models perform well in real-world scenarios. By organizing results in a test matrix and using built-in scoring tools, Braintrust allows for the detection of performance regressions and supports iterative improvements. The platform also provides a comprehensive UI for managing datasets, prompts, and model comparisons, making it easier for teams to maintain traceability and reproducibility in their AI development workflows.

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