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Define useful behavior before choosing a success metric.
Evaluation connects a model result to the task it is supposed to perform. This tag covers classification decisions, structured-output validation and the evidence needed for tokenized-data workflows. Start with the outcome that matters to a caller, then work backward to the examples and checks that make it testable.
Keep representative cases separate from examples used to tune a prompt or policy. Include missing fields, ambiguous inputs and unusual but valid records. The guides distinguish structural validity from semantic correctness: an output can satisfy a schema and still fail the task. A review path is part of the design when uncertainty cannot be resolved automatically.
3 articles tagged Evaluation
Build traceable AI data workflows for tokenized assets by preserving source identity, observation times and derivation records. Separate claims from conclusions and evaluate classification with realistic evidence gaps. Connect each model result to the records that supported it.
7 min readConnect prompt design to an explicit output contract. Use schemas, unavailable states, source-reference checks, bounded corrections, and focused evaluation cases to turn generated text into records that an application can validate.
7 min readTurn classifier outputs into decisions that can be evaluated. Define label semantics, distinguish scores from calibrated probabilities, choose review thresholds, and measure precision, recall, coverage, and failures across realistic inputs.
7 min read
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