The article delves into the complexities and challenges associated with Natural Language Processing (NLP) and its counterpart, Natural Language Understanding (NLU), in helping computers comprehend human language. A key difficulty lies in the fluidity and inconsistency of language, with context playing a significant role in meaning, which is often challenging for NLP models to grasp. The text highlights techniques such as word and contextual embedding, which aim to improve understanding by representing words as vectors to capture context better, although techniques like word2vec face limitations in handling polysemous words. Additionally, the article discusses spelling correction using cosine similarity and emphasizes the importance of high-quality data input, noting processes like text standardization, lemmatization, stemming, and tokenization to enhance data quality for improved model accuracy.