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JevMade field notes / Reliability guide

Use confidence without confusing it with correctness

Confidence summarizes how strongly one answer stands out from the others. It can help a program choose when to act or check again, but it is not proof.

Original by TypeSafe AIGuardrails

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Our summary

When Jev chooses from a list or rates something, it gives each possible answer a probability: an estimate of its likelihood. Confidence summarizes how those estimates are spread. It is higher when one answer stands out and lower when several are close together.

A program can use that number to choose among acting, checking again, and stopping for help. Where it draws the lines should depend on the task. A costly or irreversible action needs more caution than a suggestion someone can easily ignore or change.

A confident answer can still be wrong. Test the decision rules on examples with known answers, and keep ordinary permission checks in place. For a yes-or-no Noul question, Jev returns just the likelihood of yes; a separate confidence number would not add another outcome.

Key takeaways

  1. Confidence describes how clearly an answer leads, not whether it is true.
  2. Use more cautious action rules when mistakes are costly.
  3. Check performance on your own examples before relying on a cutoff.

TypeSafe supplies this confidence measure, but programs can also examine the individual probabilities and choose another way to summarize uncertainty.

Official TypeSafe documentation · Source reviewed

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