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All digests tagged LLM
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Learn how to evaluate AI agent skills using SkillEvaluator. This multi-tier framework checks for security risks (prompt injection, data exfiltration) and measures 'Skill Lift' across five dimensions to ensure reliability in production workflows.
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MLflow provides deep observability for multi-agent systems, moving beyond simple HTTP monitoring to trace internal LLM calls, tool usage, and complex failures like non-determinism. Learn how to use LLM judges and integrate tracing into CI.
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Learn how Research Preference Models (RPMs) use LLMs to guide autonomous ML experiments, drastically cutting compute time from days to hours by predicting the most promising research paths.
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Cedric Clyburn explains why LLM benchmarks don’t reflect real-world performance in AI applications and agents. Learn how to evaluate accuracy, latency, and cost to build reliable AI systems at scale.
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