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How custom evals get consistent results from LLM applications
Public benchmarks are designed to evaluate general LLM capabilities. Custom evals measure LLM performance on specific tasks.
With simple instructions and prompt engineering techniques, you can get an LLM to perform tasks that would have otherwise required training custom machine learning models. Evals typically involve presenting the model with a set of carefully crafted inputs or tasks, then measuring its outputs against predefined criteria or human-generated references. Unlike the generic tasks that public benchmarks represent, the custom evals of enterprise applications are part of a broader ecosystem of software components.
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