Braintrust
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An evaluation-first platform for AI applications — build eval datasets, run scored experiments, and monitor quality in production.
Overview
Braintrust treats evaluation as the center of AI development rather than an afterthought. Teams build datasets, define scoring functions, and run experiments so a prompt or model change is judged by measured quality instead of vibes — with a playground, logging, and production monitoring around it. For enterprises this is the discipline that separates AI features that ship reliably from ones that quietly regress. The work it does not remove is designing good evals and scorers; that judgment remains the hard, human part.
Pros & Cons
Pros
- Evaluation-first workflow for AI development
- Scored experiments replace guesswork
- Playground, logging, and production monitoring
- Catches quality regressions before users do
Cons
- Designing good evals and scorers is still hard work
- Most valuable once you have an eval culture
- Yet another platform in the AI toolchain
Workflows that use Braintrust
Get a new AI workflow each week — many feature Braintrust and other tools in this category.