1562646 Comments

• Comment Link الأحد, 11 تشرين1/أكتوير 2026 12:05 posted by Melvinlog

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• Comment Link الأحد, 11 تشرين1/أكتوير 2026 12:05 posted by AI_Engineer_Hali

  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.
  
  The plain service reference is https://ai-software-development.net. RAG development guidance can help frame the architecture, while enterprise RAG engineering provides the BBCode option.
  
  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.
• Comment Link الأحد, 11 تشرين1/أكتوير 2026 12:04 posted by Jamesheexy

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