Introducing generative AI

Engineers have been working on developing probabilistic natural language models for decades [1]. We need such models to solve various tasks related to understanding human language, also known as natural language processing (NLP) tasks – translation, optical character recognition (OCR), summarization, generating new text, extracting entities from text, and more.

In the last 5 years, we’ve observed a breakthrough in NLP related to generative AI and LLMs. Generative AI is one of the hottest topics right now – it’s mentioned in over 40% of SP&500 analyst calls [2]. From this, we can observe a huge interest not only from startups and consumers but also from enterprises as they want to move fast and adopt new technology. It also drives interest across practitioners.

In this book, we discussed how to develop an enterprise-ready generative AI solution on Google Cloud with LangChain, but in this Appendix, we’d like to spend...