Braintrust

Braintrust is an AI evaluation platform focused on the specific challenge of measuring whether an AI application is actually getting better or worse as prompts, models, and logic change. This problem is harder than it sounds: AI output quality is subjective, variable, and difficult to measure with traditional testing approaches.

The platform provides tools for building and managing evaluation datasets — collections of inputs with expected outputs or scoring criteria. Running an experiment compares the AI application's outputs against these expectations, providing quantitative metrics for quality across dimensions like accuracy, relevance, and tone. Comparing two experiments (old prompt versus new prompt, GPT-4o versus Claude) gives teams objective data for decisions that previously relied on intuition or manual sampling.

The scoring system is flexible: human annotation for subjective quality, LLM-based evaluation where a judge model scores outputs, custom Python functions for domain-specific metrics, or reference answer comparison. This flexibility allows teams to measure what actually matters for their specific application rather than generic metrics.

Braintrust integrates with LangChain, LlamaIndex, and directly with AI model APIs, capturing traces from production for analysis and continuous evaluation. The playground allows testing prompt variations interactively before committing to formal experiments.

The open source `autoevals` library provides reusable evaluation functions that teams can run locally or within Braintrust, reducing the setup overhead for common evaluation patterns.

For AI product teams that take quality seriously and want systematic rather than ad hoc evaluation, Braintrust provides the infrastructure that transforms AI quality assessment from a subjective discussion into a measurable practice.