Evaluation & Monitoring of LLM Systems
Benchmarks, hallucination checks, guardrails, telemetry, and human review for safe and reliable AI deployment
Benchmarks, hallucination checks, guardrails, telemetry, and human review for safe and reliable AI deployment
Evaluating and monitoring Large Language Models (LLMs) ensures accuracy, safety, and responsible AI behavior. This includes automated testing, continuous monitoring, and human oversight.
Standardized datasets and tasks that measure LLM accuracy, reasoning, safety, and robustness.
Detection of incorrect or fabricated model outputs through automated or human validation.
Safety layers, filters, and policy frameworks to prevent harmful or unintended outputs.
Real-time monitoring of model usage, performance, anomalies, and quality signals.
Expert oversight for evaluating complex outputs, refining safety rules, and identifying edge cases.
Specify accuracy, safety, and reliability objectives.
Run against standardized metrics and datasets.
Measure factual consistency and identify failures.
Integrate safety filters and policy rules.
Telemetry + human evaluation in production.
Ensure LLMs meet legal and policy standards.
Monitor for hallucinations and sensitive data leakage.
Track user interactions and maintain reliability.
Model behavior changes with new data and user interactions, requiring continuous oversight.
Ideally after each model update and periodically during production.
Knowledge gaps, ambiguous prompts, or model overconfidence.
Implement rigorous evaluation, monitoring, and human oversight to ensure trustworthy LLM performance.
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