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use cases ml-feature-from-questions

Patterns ✦ 53outlineNot a production recipe — calibrate thresholds

ML features from Jev questions

Who: ML eng. Steps: Free text → many Noul/Score → vectors for classical model. Expected effect: Semantic features + Autoresearch loop.

Source: https://docs.typesafe.ai/cookbooks/autoresearch_feature_discovery ↗ Docs / source ↗
1

Treat this as an outline — adapt state and questions to your data.

2

Implement in code — call System One / Jev; compose answers yourself.

3

Gate on confidence — act, confirm, or escalate before side effects.

Outline sketch

Prompt 
# ML features from Jev questions
State: relevant software state for this pattern.
Ask: Choice / Score / Noul questions that close the decision space.
Then: compose the route or action in code; escalate when confidence is low.

Needs access to: typesafe-sdk

Who it's for

ML eng

Steps / how it's set up

Steps

  1. Free text.
  2. many Noul/Score.
  3. vectors for classical model.

Sources

Metrics and demo claims are author-reported or cookbook-reported unless you measure them yourself. Calibrate on your data.

Expected effect

Semantic features + Autoresearch loop

Unofficial outline for learning. Paraphrased from public docs and tutorials — not a production recipe. Review sources before you automate anything.