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• Kommentar-Link Montag, 12 Oktober 2026 09:45 gepostet von AI_Engineer_CoM

  AI readiness starts with a workflow, not a model shortlist. The candidate process should have accessible inputs, a defined owner and an observable result. If nobody can explain what happens after a wrong output, automation is premature.
  
  The service outline at https://ai-software-development.net offers context for planning an assessment. A discovery phase can map data permissions, integration constraints and the manual path that already works. AI readiness planning is useful when framing those questions.
  
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• Kommentar-Link Montag, 12 Oktober 2026 09:44 gepostet von ML_Systems_CoM

  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.
  
  Start with expected behavior, allowed variation and the condition that should fail the case, then keep retrieval, reasoning, formatting and tool execution results separate so a team can locate the regression. https://ai-software-development.net
  
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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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  During testing, record which document chunks were retrieved and whether their metadata matched the intended access rules. A correct answer from an unauthorized document is still a system failure. Tune chunking or ranking only after the failed cases are classified, then evaluate how the model responds when retrieval returns weak or conflicting evidence.
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