What is AI Evaluation (Evals) in Applied AI?
The systematic measurement of LLM application behavior against a defined task and dataset. Evaluation covers capability (does the model do the task), safety (does the model refuse where it should), fairness (does the model perform across groups), and grounding (does the model cite its sources correctly).
Why AI Evaluation (Evals) Matters for Your AI or Cybersecurity Career
Evals are the only way to know whether a prompt change or model swap actually improves the application. Production AI engineers ship evaluation suites alongside the application itself.
Related Applied AI Terms
The systematic measurement of LLM application behavior against a defined task and dataset. Evaluation covers capability (does the model do the task), safety (does the model refuse where it should), fairness (does the model perform across groups), and grounding (does the model cite its sources correctly).
Evals are the only way to know whether a prompt change or model swap actually improves the application. Production AI engineers ship evaluation suites alongside the application itself.
Sources
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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