AI is rapidly reshaping the world, and with it, a new set of specialized terms has emerged to describe how these systems work. Whether you're at a product meeting, listening to a pitch, or watching a panel discussion, you're likely to hear terms like LLMs, RAG, and RLHF being tossed around. Recently, even more technical terms like "opaque recurrence" have entered the lexicon, particularly with the release of OpenAI's Astra model. These terms move quickly, sometimes leaving even seasoned tech professionals feeling a bit out of their depth. This glossary aims to clarify these terms in plain English, helping you keep up with the fast-paced world of AI, whether you're building AI tools, investing in them, or just trying to follow the news. Artificial general intelligence (AGI) refers to AI systems that can perform a wide range of tasks as well as, or better than, an average human. While definitions vary slightly between organizations like OpenAI and Google DeepMind, the general idea is that AGI would be capable of doing most economically valuable work. However, AGI is still a theoretical goal, and researchers are still working to understand what it truly means and how to achieve it. An AI agent is a system that can perform tasks on your behalf using AI technologies. Unlike a basic chatbot, an AI agent can handle complex tasks like booking a restaurant, writing code, or managing expenses. These agents can use multiple AI systems to complete multi-step tasks, though the infrastructure to fully realize their potential is still being developed. As AI agents become more advanced, they are increasingly able to interact with software through API endpoints, which act like hidden "buttons" that allow other programs to control a system without human input. Chain of thought reasoning is a method used by large language models to solve complex problems by breaking them down into smaller, intermediate steps. This approach improves accuracy, especially in logic or coding tasks, but it takes longer to generate an answer. It's often used in conjunction with reinforcement learning, a training method where AI systems learn by trying different approaches and receiving feedback in the form of rewards or penalties. This technique is central to training AI models to be more helpful, accurate, and safe.