We reviewed two papers on ColBERT. We review and expand upon ColBERT, a neural information retrieval model that utilizes contextualized late interaction over BERT to estimate relevance between queries and documents. The first source, the original ColBERT paper, details its architecture, which encodes queries and documents into multi-vector representations and computes relevance by summing the maximum similarities between query and document token embeddings. It highlights ColBERT's efficiency for re-ranking and end-to-end retrieval compared to earlier methods. The second source introduces ColBERTv2, an enhanced version that improves upon its predecessor by incorporating residual compression to significantly reduce the model's storage footprint and denoised supervision for better quality, particularly in zero-shot generalization to various domains, establishing new state-of-the-art results while maintaining computational competitiveness.