What is AI?

The complete, sourced definition of artificial intelligence — what it is, how LLMs work, and what agentic AI adds in 2026.

Last verified: April 2026 · Official sources only · Clarigital

The one-sentence definition

Artificial Intelligence (AI) is the development of computer systems that can perform tasks which typically require human intelligence — such as understanding language, recognising patterns, making decisions, and generating content.

The distinction that matters in 2026: Most AI people encounter today is not general intelligence — it is narrow AI, highly capable at specific tasks it was trained on, with no understanding, consciousness, or goals of its own.

The types of AI you actually encounter

Large Language Models (LLMs) — AI systems trained on vast text datasets to understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They predict the most likely next word or sequence of words based on patterns learned during training.

Generative AI — AI that creates new content: text, images, audio, video, code. LLMs are a subset of generative AI. Other examples include Midjourney (images), Sora (video), and ElevenLabs (voice).

Agentic AI — AI systems that can take actions in the world — using tools, executing code, searching the internet — to complete multi-step tasks without requiring a human prompt at every step. The most significant development in AI since the release of ChatGPT.

Machine Learning (ML) — the broader field of systems that learn from data rather than being explicitly programmed. LLMs are a type of machine learning model.

How large language models work

An LLM is trained by processing enormous quantities of text — web pages, books, code, scientific papers — and learning the statistical relationships between words and sequences. During training, the model adjusts billions of numerical parameters to get better at predicting what comes next in a sequence. After training, the model can complete, continue, or respond to any text input by generating the most probable continuation based on what it learned.

LLMs do not store facts as discrete records. They encode statistical patterns. This is why they can sound authoritative while being wrong — they generate plausible text, not verified truth. This is called hallucination: a confident, fluently written statement that is factually incorrect.

What AI can and cannot do (2026)

AI excels at: understanding and generating language, summarising large documents, translating between languages, writing and explaining code, identifying patterns in data, creating images and video from descriptions, answering questions on topics it was trained on.

AI cannot: think or reason in the human sense, verify its own outputs against reality (without tools), reliably remember previous conversations without external memory systems, act in the world without tools, or understand meaning the way humans do.

The major AI tools (2026)

ChatGPT (OpenAI) — the most widely used AI assistant globally

Claude (Anthropic) — known for long-context handling and instruction-following

Gemini (Google) — integrated across Google Workspace and Search

Copilot (Microsoft) — integrated across Microsoft 365 and Bing

Perplexity — AI-powered search with real-time web access

Agentic AI — what is changing in 2026

The most significant current development is the shift from AI that responds to prompts to AI that completes projects. Agentic AI systems can be given a goal — research this company, write this report, monitor this campaign — and execute the required steps autonomously: searching the web, reading documents, calling APIs, writing and running code, and producing a final output. This requires understanding not just what to say, but what to do next.

Frequently Asked Questions

What is the difference between AI and machine learning?

Machine learning (ML) is a subset of AI — it refers specifically to systems that learn from data. AI is the broader category that includes ML, but also rule-based systems, expert systems, and other approaches. All machine learning is AI, but not all AI is machine learning. Large language models like ChatGPT are a type of machine learning called deep learning.

What is a prompt in AI?

A prompt is the input you give to an AI system — the question, instruction, or context that tells it what to do. Prompt engineering is the practice of crafting prompts to get better results from AI models. The quality of the output is heavily influenced by the quality of the prompt.

What is the difference between ChatGPT and Claude?

Both are large language models. ChatGPT is made by OpenAI; Claude is made by Anthropic. They differ in training approach, safety philosophy, context window size, and performance on specific task types. Claude is generally noted for longer context handling and careful instruction-following. ChatGPT has a larger ecosystem of plugins and integrations. Both are updated regularly.

Is AI safe?

Current AI tools are generally safe for everyday use. Risks are primarily around: accuracy (AI can be wrong confidently), privacy (data entered into AI tools may be used for training), and misuse (AI can generate misleading content). For agentic AI systems that take real-world actions, additional safety considerations apply — these are documented in the OWASP Top 10 for Agentic AI (2026).