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  A single quality score can hide the failure that matters most in a financial workflow. Tests should separate retrieval errors from calculation errors. Policy violations and unsupported statements need their own labels. This FinTech AI evaluation approach helps define failures by their operational consequence.
  
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• Kommentar-Link Montag, 12 Oktober 2026 09:44 gepostet von AI_Engineer_CoM

  An evaluation set should represent the decisions the product actually makes. Mixing harmless wording differences with unsafe tool calls in one average can conceal a release blocker. An AI evaluation framework can assign each test case to a named failure mode.
  
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• Kommentar-Link Montag, 12 Oktober 2026 09:43 gepostet von AI_Dev_CoM

  RAG evaluation should separate retrieval failure from generation failure. If the right passage never reaches the model, prompt changes can improve tone without fixing the answer. Start with a small evaluation set that identifies the expected source for each query.
  
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• Kommentar-Link Montag, 12 Oktober 2026 09:41 gepostet von AI_Dev_CoM

  A prototype can prove that a model responds to sample inputs. An MVP has to fit a real workflow and expose enough behavior for product owners to judge it. Start with AI MVP engineering considerations when defining that boundary. The result still needs traceable inputs and a known owner. It also needs a fallback action.
  
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