Artificial intelligence (AI) is the broad field of simulating human intelligence in machines, machine learning (ML) is a subset where systems learn patterns from data, and deep learning is a further subset using multi-layer neural networks. The latest leap, generative AI powered by foundation models like large language models, can create new text, images, audio, or video, enabling applications from chatbots to deepfakes, and driving rapid AI adoption worldwide.
Foundation models, such as large language models, are trained on massive amounts of unstructured data, enabling them to transfer across many tasks through tuning or prompt engineering. They deliver strong performance and productivity gains but face challenges like high compute costs and potential bias, and are being applied across domains from language and vision to code, science, and climate research.
A large language model (LLM) is a type of foundation model trained on massive text datasets using transformer architecture to understand and generate human-like language. By learning to predict the next word in context, LLMs can be fine-tuned for tasks such as chatbots, content creation, and code generation, offering wide-ranging business applications.
RAG enhances LLMs by retrieving relevant, up-to-date sources before responding, improving accuracy and reducing hallucinations.