Braintrust
AI evaluation and testing platform for LLM applications with experiment tracking, human annotation, automated scoring, and production tracing.
Braintrust is a developer platform for evaluating and improving AI applications built on LLMs. It provides experiment tracking for A/B testing prompts, models, and parameters; a human annotation interface for collecting labeled data; LLM-as-judge automated scoring for scalable evaluation; and production tracing for monitoring live AI applications. Teams use Braintrust to measure whether a new prompt, model, or system change actually improves output quality before shipping it to users. The platform manages evaluation datasets, stores results over time for trend analysis, and provides a prompt playground for side-by-side model comparisons.
Key Features
- Experiment tracking for A/B testing prompts, models, and system parameters
- Human annotation UI for collecting labeled preference data from reviewers
- LLM-as-judge automated scoring for high-throughput evaluation without full human review
- Production tracing for logging and debugging real LLM calls in live applications
- Prompt playground for comparing model outputs side-by-side across providers
- Dataset management for versioning and reusing evaluation sets across experiments
Use Cases
- AI engineers measuring whether prompt or model changes improve output quality before shipping
- Teams collecting human preference data for fine-tuning and reward model training
- Developers debugging production AI failures by tracing inputs, outputs, and latency
- Product teams A/B testing different AI configurations to optimize user-facing quality
Pros
- Combines experiment tracking, human annotation, and production tracing in one integrated platform
- LLM-as-judge scoring enables high-throughput evaluation at a fraction of full human review cost
- Free tier supports meaningful evaluation work without requiring enterprise contracts
Cons
- Setup requires engineering effort to define meaningful eval functions for specific tasks
- Human annotation UI is functional but less polished than dedicated data labeling platforms
- Trace storage costs scale with production traffic volume for high-throughput applications
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