Recent advancements in generative AI have made text-guided image inpainting—adding, removing, or altering image regions using textual prompts—widely accessible. However, generating semantically correct photorealistic imagery, typically requires carefully-crafted prompts and iterative refinement by evaluating the realism of the generated content - tasks commonly performed by humans. To automate the generative process, we propose Semantically Aligned and Uncertainty Guided AI Image Inpainting (SAGI), a model-agnostic pipeline, to sample prompts from a distribution that closely aligns with human perception and to evaluate the generated content and discard one that deviates from such a distribution, which we approximate using pretrained Large Language Models and Vision-Language Models. By applying this pipeline on multiple state-of-the-art inpainting models, we create the SAGI Dataset (SAGI-D), currently the largest and most diverse dataset of AI-generated inpaintings, comprising over 95k inpainted images and a human-evaluated subset. Our experiments show that semantic alignment significantly improves image quality and aesthetics, while uncertainty guidance effectively identifies realistic manipulations — human ability to identify inpainted images from real ones drops from 74\% to 35\% in terms of accuracy, after applying our pipeline. Moreover, using SAGI-D for training several image forensic approaches increases in-domain detection performance on average by 37.4\% and out-of-domain generalization by 26.1\% in terms of IoU, also demonstrating its utility in countering malicious exploitation of generative AI. Code and dataset will be publicly released.