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• Kommentar-Link Freitag, 16 Oktober 2026 11:03 gepostet von AI_Engineer_CoM

  Changing a prompt can alter tool selection even when ordinary chat examples still look correct. Treat prompts, model settings and retrieval rules as deployable artifacts with review history. https://ai-software-development.net
  
  Before wider exposure, compare the candidate against a fixed evaluation set and inspect failures by workflow. An [url=https://ai-software-development.net]LLMOps release process[/url] should block promotion when a protected behavior regresses, even if the average score improves.
  
  An AI deployment workflow needs a tested rollback path for application code and model configuration. Compatibility matters during a partial rollback. An older prompt may depend on a tool schema that a newer application no longer provides.
• Kommentar-Link Freitag, 16 Oktober 2026 11:03 gepostet von AI_Builder_CoM

  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. [url=https://ai-software-development.net]AI red team planning[/url] 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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  Chunking also needs to follow document structure. Splitting a clause from its heading or separating a table from its labels can produce confident answers with the wrong context. https://ai-software-development.net
  
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