JevMade field notes / Written guide

Design decisions before choosing a model

Augustus teaches a planning method for jobs that might use rules, a classifier, a decision model, or a person. It starts with the outcome that matters and asks whether another judgment would actually change the action, rather than assuming every uncertain step needs AI.

Original by 24601Evaluation

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Credits and license

“Augustus” by 24601. Read the original source.

This expanded guide is an AI-narrated adaptation of the source’s essential explanation, examples and caveats, not a word-for-word reading. The synthetic voice does not imitate the author or imply their endorsement.

License: MIT

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Our summary

Augustus begins by naming the outcome, action, and cost of a mistake. It then asks where another judgment would change the result and whether rules, search, a model, or a person belongs at that point. This keeps the method focused on placement rather than model choice.

The method compares a baseline—a simpler approach used for comparison—and lets the system abstain, meaning decline to decide and ask a person. It builds tests from real labelled cases, measures outcomes, and sets a stopping rule before further improvement work.

This is a design method, not proof that Jev or another model works for a particular job. Teams need representative examples, honest outcome tracking, fixed limits for high-impact actions, and a reason to stop when added complexity does not improve the result.

Key takeaways

Start with the action and its consequences, not the model.
Compare AI judgment with rules, search, and human review.
Decide when to decline, how to test, and when to stop improving the design.

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