The AI industry has developed a vocabulary that can make even technically literate people feel lost, and the gap between understanding the headlines and understanding what is actually happening has never been wider. A few key terms are worth knowing because they come up constantly and shape how the entire field works. A large language model, or LLM, is the core technology behind ChatGPT, Claude, Gemini, and most other AI assistants you interact with daily — a deep neural network trained on billions of pieces of text that learns to predict the most likely continuation of any prompt you give it. Training is the process of feeding data into a model so it can learn patterns, and inference is what happens when the model is actually running and generating responses. These two processes have very different hardware requirements and cost profiles, which is why you hear so much about compute — the GPUs, TPUs, and other specialized chips that power both. Hallucination is the term the industry uses for when AI models confidently generate false information, which happens because of gaps in training data and remains one of the most significant unsolved problems in the field.

Chain-of-thought reasoning is one of the more important recent developments worth understanding: it refers to breaking a problem into intermediate steps before arriving at an answer, similar to writing out the working when solving a math problem. Models that use chain-of-thought reasoning take longer to respond but produce significantly more accurate results on logic and coding tasks. Reinforcement learning is the training technique behind many of the recent improvements in model capability — the system learns by trying things and receiving feedback signals indicating success or failure, similar to training a pet with rewards but at mathematical scale. Fine-tuning is how companies take a general-purpose model and optimize it for a specific domain or task by feeding in specialized data, which is why there are now AI models trained specifically for medicine, law, finance, and dozens of other verticals. Together these concepts form the foundation of almost every significant AI product being built right now, and understanding them makes the rest of the industry's development significantly easier to follow.