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An overview of practical troubleshooting checklists for AI projects. It outlines systematic steps to diagnose and fix issues across data quality and labeling, model configuration, training and inference, prompts and evaluation, and deployment. Emphasizes reproducibility, logging, monitoring, bias and safety checks, and provides templates and playbooks to speed incident response and improve reliability.