The architecture and the intuition behind each operation are what imparts BERT the advanced capacity to identify the context of a sentence. It does so by employing the multi-head attention mechanism. Attention, by definition, is a weighted average of value that provides numerical information obtained by jointly learning pieces of data . It includes a set of common mathematical operations such as matrix multiplication and addition to obtaining different latent features from the same input sequence. BERT has 24 layers with 340M parameters with a total size of 1.34 GB. With training, depending on the inputoutput, the overall network learns to investigate a set of keywords that explain the whole sentence and thereby remove the stop-words that don't fit into the context. BERT has shown its impeccable result in 11 different types of language processing model that includes language inference and question answering . Comparing the embeddings of each word after training reflects the relationship of words in a sentence. Thus, while any other encoder-decoder algorithm can be tuned into a question-answering module, the specific internal architecture of the encoder and the decoder subblocks of BERT make it powerful enough to extract the context out of a text. In this work, we use CDQA to identify the relevant paragraphs on perovskite synthesis that serve as a metadata for further analysis. The 'Document Retriever' scans the given pool of articles to filter out the 'N' most relevant documents to the given question. The 'Document Reader' processes these documents to get the closest possible answers. In this work, we extracted 3 answers from each article. We also acquired the corresponding paragraphs where the answers are based and used them to get the perovskites and the solvents. Answers with higher scores appear more relevant than the others.