Braintrust provides an end-to-end platform for developing and evaluating large language model (LLM) applications, utilizing iterative workflows to track prompt performance and model outputs. This technology addresses the challenges of non-deterministic AI systems by enabling real-time monitoring, debugging, and integration of evaluation metrics into the development lifecycle.
Funding
$36M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Developing robust applications using Large Language Models (LLMs) is challenging due to their non-deterministic nature and the difficulty in predicting their behavior with varied natural language inputs. Traditional development workflows lack the necessary tools to effectively evaluate, debug, and monitor LLM performance, leading to unpredictable outputs and potential errors in production.
Solution
Braintrust offers an end-to-end platform designed to streamline the development and evaluation of LLM-powered applications. The platform provides iterative workflows for tracking prompt performance and model outputs, enabling real-time monitoring and debugging. By integrating evaluation metrics directly into the development lifecycle, Braintrust helps teams adapt their processes for the AI era, ensuring more reliable and predictable LLM application behavior. The platform allows users to easily identify regressions caused by prompt changes or model updates, facilitating continuous improvement and optimization.
Target Audience
Braintrust is designed for AI product teams, including both technical and non-technical members, who are building and deploying applications powered by Large Language Models.
Features
- Real-time visualization and analysis of LLM execution traces for debugging and optimization
- Tools for monitoring real-world AI interactions, providing insights into production performance
- Automated, asynchronous server-side scoring for continuous evaluation via online evals
- Support for defining custom scorers and callable tools using TypeScript and Python functions
- Option for self-hosting to maintain full control over data and compliance requirements
- Prompt management features for tweaking LLM prompts and tracking their performance over time
- Dataset management for capturing, versioning, and securing rated examples from staging and production