Content creators often want to use personalized themes to create unique images, going beyond the capabilities of traditional text-to-image models. Moreover, they may desire images that incorporate specific locations, styles, or moods. Existing personalization methods may compromise between personalization capabilities and alignment with complex textual prompts. This trade-off can hinder the fidelity of user prompts and thematic representation. We propose a novel method, focusing on single-prompt personalization, to address this issue. We call this method Prompt-Aligned Personalization. While seemingly limited, our method excels in improving text alignment, enabling the creation of images with complex and intricate prompts, something challenging for current technologies. Specifically, our method utilizes additional score distillation sampling terms to ensure the personalized model remains aligned with the target prompt. We showcase the versatility of our method across both multi-shot and single-shot settings, further demonstrating its ability to combine multiple themes or draw inspiration from reference images like artworks. We provide both quantitative and qualitative comparisons against existing baselines and state-of-the-art techniques."