The core of effective AI product development is not prompt engineering or model selection, but a rigorous, continuous focus on evaluations ('evals') to define and measure desired outcomes.
Braintrust achieved early success by targeting high-taste customers (Stripe, Instacart, Airtable) and building a culture of radical customer obsession, where engineers prioritize fixing user issues over rigid sprint plans.
Standard data infrastructure is ill-equipped for AI workloads; Braintrust built a custom data system, Brainstore, to handle the unique scale and complexity of LLM-generated text and JSON.
The future of AI development involves using AI to simplify the process itself, with models now capable of evaluating and improving their own work, a trend Braintrust is embracing with its internal agent, 'Loop'.
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Concerns Raised
Incumbent companies face an existential threat if they fail to rebuild their products around AI.
Standard data infrastructure is not equipped to handle the scale and complexity of modern AI workloads.
The unpredictable nature of LLMs makes it difficult to build quality products without a rigorous evaluation framework.
Opportunities Identified
The shift of all software development towards AI creates a massive market for specialized developer tools like Braintrust.
The increasing capability of AI models to evaluate and improve themselves will simplify product development and enable self-optimizing systems.
A model-agnostic platform is a significant competitive advantage in a rapidly evolving and fragmented AI model landscape.