Benchmarking AI decision models against traditional guardrails | Red Hat Developer
- As enterprise generative AI applications move to production, platform engineers face a key challenge: balancing the flexibility of LLM-as-a-judge guardrails with the reliability and portability of traditional classifiers that require custom training data.
- The recent emergence of "decision models"—highlighted by TypeSafe AI's recent announcement of Jev and "System One" models—promises a flexible middle ground by producing fixed "decisions" given a state and a list of questions rather than generating text.
- A trivial example of using a decision model (adapted from John Berryman of Arcturus Lab's
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- As enterprise generative AI applications move to production, platform engineers face a key challenge: balancing the flexibility of LLM-as-a-judge guardrails with the reliability and portability of traditional classifiers that require custom training data.
- The recent emergence of "decision models"—highlighted by TypeSafe AI's recent announcement of Jev and "System One" models—promises a flexible middle ground by producing fixed "decisions" given a state and a list of questions rather than generating text.
- A trivial example of using a decision model (adapted from John Berryman of Arcturus Lab's
Sources: Redhat