Evaluation & Monitoring of LLM Systems
Benchmarks, hallucination checks, guardrails, telemetry, and human review.
Explore the Framework
Benchmarks, hallucination checks, guardrails, telemetry, and human review.
Explore the Framework
Evaluating and monitoring Large Language Models is essential for safety, quality, and reliability. This includes structured testing, real-time monitoring, risk mitigation, and human oversight.
Standardized evaluations to measure model accuracy, reasoning, robustness, and task performance.
Processes that detect fabricated or incorrect model outputs using validation layers and consistency checks.
Safety layers that prevent harmful or disallowed model behavior through rules, filters, and constraints.
Real-time monitoring of user interactions, system performance, and model reliability signals.
Expert oversight to validate outputs, resolve edge cases, and ensure compliance with policies.
Run benchmarks to establish performance expectations.
Evaluate edge cases, adversarial prompts, and failure modes.
Apply safety rules to control risky model outputs.
Track reliability metrics and model drift continuously.
Escalate sensitive or ambiguous cases to human reviewers.
Ensure compliance in finance, healthcare, and legal applications.
Maintain factual accuracy and prevent misinformation in responses.
Study hallucinations, evaluate guardrails, and test new mitigation tools.
Fabricated or incorrect outputs generated by an LLM.
AI systems cannot fully understand context or risk, so humans validate sensitive outputs.
Continuously, especially in production environments where model drift can occur.
Get expert guidance on evaluation, monitoring, and safety best practices.
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