AI Video Braintrust Launches Loop to Automate AI Model Evaluation
At the AI Engineer World’s Fair in San Francisco, Braintrust CEO Ankur Goyal introduced Loop, a tool built to simplify the often tedious process of AI model evaluation. Loop aims to reduce the heavy manual workload developers face when testing and refining AI models, making the evaluation process more efficient and insightful.
The Challenge of AI Evaluation Today
AI teams run a huge number of experiments daily—Braintrust users average 12.8 experiments per day, with some exceeding 3,000 evaluations. Despite this volume, the process remains manual and time-consuming. Engineers spend over two hours each day combing through dashboards, trying to extract meaningful insights from raw data.
Goyal explained the core issue: after running an evaluation, developers mostly rely on dashboards to guide their next steps. This means manually deciding what changes to make in code or prompts, which slows down progress and can lead to overlooked improvements.
How Loop Changes the Evaluation Workflow
Loop is an AI-powered assistant integrated into Braintrust that automates many of these manual tasks. It uses advanced language models to analyze current scorers, datasets, prompts, and evaluations, then provides specific suggestions for improving prompts and generating dataset rows directly within the platform.
This hands developers actionable feedback instantly, reducing the time spent on debugging and testing. Instead of sifting through raw results, engineers can focus on applying Loop’s recommendations and iterating faster.
The Technology Behind Loop’s Effectiveness
Loop’s capabilities are made possible by recent advances in language models. Goyal highlighted Claude 4 as a milestone, noting it performs nearly six times better than earlier models. This improvement allows Loop to deliver more accurate and relevant optimization suggestions, marking a shift in how AI development teams handle evaluation.
Maintaining Developer Control and Transparency
Despite automation, Loop keeps developers in the driver’s seat. It offers side-by-side comparisons of suggested prompt and data edits, allowing teams to review and approve changes before implementation. This transparency supports responsible AI practices and ensures that engineers retain oversight.
By automating routine evaluation tasks, Loop helps AI product teams spend more time on creative problem-solving and strategic improvements, accelerating the development cycle and boosting overall productivity.
- Automates prompt optimization and dataset augmentation
- Reduces manual debugging time
- Leverages advanced LLMs like Claude 4 for better insights
- Provides clear, side-by-side editing suggestions
For professionals in product development, tools like Loop signal a move toward streamlining AI workflows—making it easier to build smarter, more reliable AI products without getting bogged down in repetitive evaluation tasks.
To explore more about AI tools that can enhance your development process, visit Complete AI Training’s AI tools collection.
Your membership also unlocks:
