When large language models are used in real workflows, accuracy and reliability matter more than creativity. AI Evaluation helps teams understand whether model responses are correct, safe, and grounded in real information. Instead of manually checking model output, evaluation frameworks can score reasoning quality, tone, factual consistency, and clarity.
This makes development faster, because teams can see immediately when a prompt, model version, or knowledge source change affects output quality. It also makes deployment safer, since hallucinated or misleading responses can be detected early.
Good AI applications don’t rely on trust alone they rely on evaluation.
Further Reading:
https://github.com/future-agi/ai-evaluation
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