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AI integration is product work as much as model work. A feature must explain what the user can do when an answer is delayed, incomplete or unavailable. <a href="https://ai-software-development.net">AI product integration</a> provides the HTML service reference. The fallback may save a draft, route the task to a person or return the user to the standard workflow.

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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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