What is braintrust
braintrust is an end-to-end platform that helps companies quickly build AI applications. Based on iterative large language model (LLM) workflows, it enables development teams to evaluate and predict model performance and adapt to the development lifecycle in the AI era. braintrus supports tuning and testing AI prompts, scorers, and datasets to optimize AI application performance. braintrust is designed to simplify AI application development, providing an intuitive interface suitable for both technical and non-technical users, and syncing with codebases.
braintrust's main features
- Prompt evaluation: Supports users in tuning and testing prompts from different AI providers, tracking changes in their performance, and helping development teams understand the impact of prompts on model output.
- Scorers: Supports using industry-standard automatic scorers, or custom scorers built with code and natural language, to evaluate model output and generate scores.
- Dataset management: Captures scoring examples from staging and production environments and consolidates them into "golden" datasets, ensuring dataset version control, scalability, and security.
- Integrated workflows: Seamlessly integrates the evaluation process into mainstream engineering workflows, helping teams perform effective quality control during development.
braintrust's official website address
- Official website address：braintrust.dev
braintrust's product pricing
- Builder: Free, 1,000 "spans" per week, a prompt playground with custom scorers and tools, shareable experiments, and support for up to 5 users.
- Enterprise: Custom pricing, on-premises deployment or private VPC (Virtual Private Cloud), and a shared Slack channel.
- Open source and .edu: Free, all features in the Builder plan, unlimited users, and requires registering with a .edu email address or contacting the platform to obtain free access for open source projects.
braintrust's use cases
- AI product development: Enterprises developing new AI products use iterative testing and evaluation to optimize product performance.
- Model evaluation: When introducing new models or updating existing models, use braintrus to evaluate model effectiveness and ensure model output meets expectations.
- Quality control: During the development and deployment of AI applications, perform quality control to ensure the accuracy and consistency of output.
- Dataset construction: Build and maintain high-quality datasets for training and testing AI models, improving model generalization ability.
- Automated testing: Automatically test AI application responses and use scorers to evaluate how well output matches expected results.