Five hard-learned lessons about AI evals

The team at Braintrust focuses on leveraging evaluation data to help organizations efficiently deploy LLM-powered products, offering a platform that supports comprehensive evaluation and observability workflows. They have identified key lessons from their experience, emphasizing the importance of effective evaluations, engineering great evals, prioritizing context over prompts, being adaptable to new models, and optimizing the entire evaluation loop. Their approach includes integrating real user data, designing LLM-friendly tools, and maintaining continuous evaluations to anticipate technological shifts. Braintrust's platform, including its AI agent Loop, is designed to streamline evaluation processes, enabling rapid model updates and robust feature validation, ultimately allowing teams to focus on delivering features their users love.

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