Frequently Asked Questions

BasicsOf.AI is a free, plain-language reference for artificial intelligence. It explains the concepts, terms, and tools behind modern AI for people who want to understand the field without learning to code. You can start with the basics or browse every topic.

Anyone curious about AI. The writing assumes no technical background and no maths. If you have used a chatbot and want to know what is actually happening, or you keep hearing words like model, token, and agent and want them explained clearly, this is written for you.

Yes, it is free, and there is no account to create. There is no sign-up, no paywall, and no advertising. You can read everything straight away.

The content is researched and written by INAGAWA, then checked against primary sources before it is published. The questions below explain how that checking works.

Every factual claim is checked against primary sources, meaning the original documentation, research papers, model cards, and technical specifications, rather than other articles or summaries. A claim that cannot be traced to a primary source does not get published. Each article lists the sources it draws on at the bottom of the page.

AI moves quickly, so articles separate the durable ideas from the fast-changing details. Time-sensitive articles carry a snapshot date that shows when the facts were last verified. Explanations that do not go out of date are marked evergreen.

Please tell us. Accuracy is the point of the site, and a correction with a source is genuinely useful, especially one we can check.

No. The site is neutral and vendor-independent. It explains how things work and what the tradeoffs are, without promoting any company or product.

They are nested, not interchangeable. Artificial intelligence is the broad field, machine learning is one approach within it, and a large language model is one kind of machine-learning system. The full breakdown is in AI vs ML vs LLM.

A large language model, or LLM, is a system trained on very large amounts of text to predict the next piece of text, which lets it answer questions, write, and summarise. What Is an LLM explains how that works in plain terms.

A model generates likely text, and likely is not the same as true, so it can state something confidently that turns out to be wrong. This is called a hallucination. Hallucination, Grounding, and Guardrails covers why it happens and how to reduce it.

It depends on the tool and its settings, and in many cases your conversations can be stored and used to improve the service. AI Privacy and Your Data explains what to check and how to limit what you share.

It depends on the task, and the honest answer is that several tools overlap. Which AI for Which Task walks through how to match common jobs to the right kind of tool.