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How recommendations are generated, scored, reviewed, approved, and declined.

  1. 1How AI recommendations workFrom crawl facts to a drafted fix: deterministic detection first, AI drafting second, human approval always.
  2. 2Confidence scores and expected impactWhat the Confidence and Impact numbers on every opportunity and recommendation actually mean.
  3. 3Reviewing and editing a recommendationUse the before/after comparison, evidence, and risk to judge a recommendation — and edit the proposal before approving.
  4. 4Approving a recommendationSend an approved change to execution — from the opportunity detail or the Approval Queue.
  5. 5Declining a recommendationReject proposals you disagree with — and what declining does and does not do.

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