A Comprehensive Guide to OpenAI, Azure, LangChain, and RAG Techniques I am writing this post because I believe Generative AI will ultimately compete with human output, playing roles such as that of data scientists. The introduction of OpenAI's ChatGPT in 2022 serves as a typical example, showcasing strong capabilities in Artificial General Intelligence (AGI). The growth of generative AI tools, including OpenAI, LLaMA, and Mistral, marks a significant evolution in the field, despite its early stage and many challenges. This progress reminds us a future where businesses across various sectors leverage Generative AI for comprehensive tasks, such as custom content creation, data analysis and predictive modeling, prompting a worrying question: what new roles and challenges await data scientists? Before 2023, I was a traditional senior data scientist. However, the occurrence of ChatGPT by OpenAI in November 2022 provided me the chance of transition into a generative AI-focused data scientist. Since March of the same year, I have been deeply engaged in learning about Generative AI, understanding the use of APIs from OpenAI, Azure OpenAI, Mistral LLM, along with LangChain and RAG techniques. This journey has enabled me to successfully execute various projects, including AI-powered chatbots, GPT agents for stock trading, and interactive predictive modeling engines. This experience led me to a recent realization: it's imperative for traditional data scientists to master Generative AI, utilizing it into their daily tasks and updating their skills to stay ahead. Reflecting on the heavy use of Python for machine learning and big data analysis back in 2015 demonstrates the importance of adapting to new technologies. Now, I aim to share my insights and experiences with newcomers and traditional data scientists alike, focusing on the critical role of Generative AI in shaping the future of data science.