793131 comments

• Comment Link Sunday, 11 October 2026 08:10 posted by AI_Engineer_et

  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.
• Comment Link Sunday, 11 October 2026 08:09 posted by Louisa Mack

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  bbc
• Comment Link Sunday, 11 October 2026 08:08 posted by p1537235

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• Comment Link Sunday, 11 October 2026 08:08 posted by Millie Gibbs

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  bbc
• Comment Link Sunday, 11 October 2026 08:07 posted by Clarence Hamilton

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  bbc
• Comment Link Sunday, 11 October 2026 08:05 posted by Georgie Briggs

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  bbc
• Comment Link Sunday, 11 October 2026 08:04 posted by Olivia Harper

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  bbc
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• Comment Link Sunday, 11 October 2026 07:59 posted by AI_Engineer_et

  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.