AI Concepts Explained: The Complete Guide

AI now covers a wide range of genuinely different technologies, and most of the confusion around AI tools comes from those terms being used interchangeably when they shouldn’t be. This guide connects the core concepts — how models are built, how they run, and where they still fail — into one map.

How AI models are built and run

• Large Language Models Explained: How They Actually Generate Text — explains how large language models actually generate text, one token at a time.
• Generative AI Explained: What Separates It From Older Forms of AI — covers what separates generative AI from the older, narrower forms of machine learning.
• AI Training Explained: How a Model Actually Learns From Data — explains training — the expensive, one-time process that builds the model in the first place.
• AI Inference Explained: What Actually Happens When You Send an AI a Prompt — explains inference — the cheaper, repeated process that happens every time you send a prompt.
• Multimodal AI Explained: How One Model Handles Text, Images, Audio and Video Together — covers multimodal AI — how one model handles text, images, audio, and video together.

Where AI still gets things wrong

• AI Hallucinations Explained: Why Chatbots Confidently Make Things Up — explains why chatbots confidently state things that are flatly untrue, and why that is an architectural property, not a bug that gets patched.

Beyond the chatbot

• AI Agents Explained: What Separates an Agent From a Chatbot — explains what actually separates an AI agent from a chatbot — the ability to take multi-step action, not just answer questions.