Generative AI is truly transforming every industry and vertical in SaaS. It significantly improves the experience of the product, the value the user receives and increases the overall productivity.
There are standard problems in implementing generative AI such as increasing accuracy and total throughput of the system. In addition, there are some unique challenges with SaaS to achieve a great experience.
In generative AI development, embeddings refer to numerical representations of data that capture meaningful relationships, semantics, or context within the data. These representations are often used to convert high-dimensional, categorical, or unstructured data into lower-dimensional, continuous vectors that can be processed by machine learning models.
Word embeddings are one of the most common types of embeddings. They represent words from a vocabulary as dense numerical vectors in a lower-dimensional space. Word embeddings capture semantic and syntactic relationships between words. For example, words with similar meanings will have similar embeddings, and word arithmetic can be performed using embeddings (e.g., "king" - "man" + "woman" ≈ "queen"). Well-known word embedding methods include Word2Vec, GloVe, FastText, and BERT.