Production traces capture where your AI falls short and what users are trying to do. Building evals with that data is how you catch failures earlier and decide what to ship next.
Braintrust is leading a workshop on how to:
- Use the patterns Braintrust surfaces automatically
- Turn them into a labeled eval dataset
- Run the same workflow every time a new pattern shows up
Vibes-based testing and manual review don't scale.
Automated evals are easy to set up and can make an immediate impact on AI development speed. Learn about three automated approaches to get started quickly with evals: LLM judges, heuristics, and comparative evals.
This is the next chapter of Braintrust: active observability. We work behind the scenes to find answers to questions before you have to ask them.
Trace everything → https://t.co/KmbQs1DgGq
Topics reconstructs conversational threads, runs the right model at the right cost, stores vectors for on-demand clustering, and surfaces the output in a UI built for humans.
Loop can create and manage dataset snapshots, tag them with environments, and prompt you to save before making changes. Your AI agent handles dataset versioning so you can focus on building better evals.
Most traditional enterprises gave responsibility for AI to their ML team, but the model providers own the data pipeline. What's left is prompt engineering, context management, distributed systems, and evals, which require a diverse set of teams to get right.
Zero-code AI observability for Java applications. Attach the Braintrust Java agent at JVM startup to automatically trace OpenAI, Anthropic, Spring AI, LangChain4j, and Google GenAI calls without touching your code.
Without validation of what good looks like, it's impossible to judge whether AI quality is improving or regressing.
Human expertise turns production traces into golden datasets that improve over time.
Most AI failures don’t appear in testing. They show up later in support tickets, vague feedback, and production traces that are hard to interpret.
Braintrust's @darubberduckiee leads a workshop on using Topics to uncover those patterns, turn them into evals, and investigate regressions before they become bigger issues.