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  Red teaming should cover the application around the model, not only adversarial prompts, because retrieved documents can contain instructions, tool outputs may carry untrusted text and authorization can fail between services. AI red team planning should trace how each input reaches a privileged action.
  
  Test whether the system follows content from an untrusted source, exposes hidden context or retries a blocked action through another tool. https://ai-software-development.net
  
  An AI security evaluation should record the attempted path and the control that stopped it. That evidence distinguishes a resilient workflow from a model that merely refused one wording. Retest the path after changes to prompts, retrieval rules or tool permissions.
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  mercredi, 14 octobre 2026 07:37 Posté par AI_Dev_quew

  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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  mercredi, 14 octobre 2026 07:36 Posté par AI_Dev_quew

  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.
  
  Keep the first test narrow enough to compare model behavior with the current process. AI discovery services can then support a discussion about scope. A workflow with unstable rules or missing source data may need process repair before any model work begins.