New Frontier data and RL environments, off the shelf
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Clear guidance for evaluating, choosing, improving, and deploying AI in real business workflows, written as the questions teams actually ask.
Evaluation gives you a structured way to answer practical questions about an AI system: Is it good enough for this workflow?
Different evaluation methods suit different kinds of tasks. The right choice depends on what you measure and how subjective the task is.
A useful evaluation should help you make a decision, not just produce a score. That starts with choosing the right tasks to test.
Choosing a model is usually a tradeoff between quality, cost, latency, reliability, and operational requirements.
Once you understand where a model succeeds and fails, the next question is how to improve it, from better prompts to changes in the model.
A model that performs well in an offline evaluation still needs to work reliably inside a real product or workflow.
