This study proposes a novel approach for personalized text-to-image generation, leveraging both textual descriptions and a small set of user provided images (3-5 examples). Specifically, we explore the technique of Textual Inversion, which transforms visual concepts from images into pseudo-words. These pseudo-words are then incorporated into the prompt, generating new images that embody the desired characteristics. Our approach enhances the ability of generative models to produce images that accurately reflect both the textual prompt and the unique features present in the user-provided examples, thus enabling a more personalized and context-aware image generation process