The Segment Anything Model (SAM) is a foundational model for Computer Vision developed by Meta AI, trained on a huge corpus of data containing millions of images and billions of masks. It has shown incredible flexibility in segmenting over wide-ranging image modalities and problem spaces. However, it was released without fine-tuning functionality, prompting the need to outline key steps to fine-tune SAM using the mask decoder. Fine-tuning is desirable to obtain better performance on specific use cases without incurring the computational cost of training a model from scratch. To fine-tune SAM, one needs to extract its underlying pieces of architecture, create a custom dataset, preprocess input data, set up the training environment, train the model, and save checkpoints for later use. Fine-tuning has shown promising results, with the fine-tuned version achieving tighter masks than the original vanilla SAM mask on previously unseen examples.