TLDR Tech / AI
Monitoring LLM behavior: Drift, retries, and refusal patterns
Monitoring LLM behavior necessitates adopting the AI Evaluation Stack, separating tests into deterministic assertions (syntax and routing integrity) and model-based evaluations (semantic quality). Engineers use offline pipelines for pre-deployment regression testing with human-reviewed 'Golden Datasets' while online pipelines monitor real-world performance for drift and failures. A continuous feedback loop from production telemetry ensures AI systems adapt, maintaining high performance as user behavior evolves.
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