Generative (Gen) AI has experienced explosive growth since ChatGPT’s release in 2022, driven by advances in several key areas. These critical areas include massive compute power, large datasets, and complex algorithms, all of which are essential for building large language models with billions and trillions of parameters. The process Gen AI uses is intricate, involving converting input prompts to tokens and embeddings, contextualizing them using self-attention, predicting outputs token-by-token, and decoding the result. Large language models come in general-purpose and domain-specific varieties, with their performance measured on various benchmarks. Despite incredible acceleration in the capabilities of large language models, careful prompt engineering and constrained knowledge bases are needed to mitigate hallucinations—a key challenge where models produce plausible but incorrect information. While generative AI has many potential applications, particularly in healthcare, addressing hallucinations and related issues such as privacy, safety, bias, and misinformation is critical. This creates the need for robust governance frameworks and human oversight for responsible implementation.