As AI systems scale, manual output review becomes impossible. LLM Evaluation Automation integrates evaluation directly into CI/CD pipelines, so every model change is tested automatically. This prevents sudden quality drops or silent hallucinations from reaching users.
Automated evaluation also creates consistency. Teams can define expected behavior, score outputs, monitor score trends, and block deployments when quality goes below defined thresholds. This is how AI systems move from “experimental” to “production-ready.”
Automation makes model behavior stable and predictable over time.
Further Reading:
https://github.com/future-agi/ai-evaluation
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