Beginners.

A system that uses AI to work towards a goal through making decisions and taking actions often using a variety of tools. The agent is specifically autonomous (or at least, partially autonomous) when it comes to many of these steps to decide and act, and only involves a human if told to do so (which is usually called Human in the Loop). Worth knowing that something like ChatGPT or a chatbot is not in itself an "agent" as it doesn't take action steps autonomously. Agents can use a variety of tools and systems and even different models, and can be multiple agents used together for different steps with different strengths, tasks and focuses (orchestration). Until recently, agents were usually used for work contexts to run workflows but with the emergence of Meta's Muse Agent, ChatGPT's dots, and Instinct personal agents are now in consumer's hands as has been long predicted. This is likely to impact commerce and ecommerce and well as person-to-person interactions

AI is the huge field of getting computers to perform tasks that used to only be performed with real (mostly human) intelligence. This can include recognising images, understanding language, finding patterns, making predictions, or planning actions. AI has been around for several decades and is thought to have been born in the 1950s, but many of its underlying ideas are much older. The span of AI includes rules-based systems, machine learning, computer vision, robotics, and generative AI. The very recent breakthrough is generative AI (or Gen AI), which is often what people mean when they say AI. Generative AI can create text, images, audio, video, and software when prompted by a human, and often takes the form of a chat interface.

Low-value AI-generated content. Usually slop means work produced quickly and in large volumes with little critical human oversight, and it often pops up in the places where we'd expect human-grade material, so the contrast makes it visible. AI slop can be images, articles, reviews, music, video, even company reports. AI slop is content with a bad ratio of considered thought to content volume. But AI generated content isn't necessarily slop, and slop isn't negative only because AI was involved. It's actually the lack of good human judgement to filter, craft and edit the AI generated content that creates the slop part. Merriam-Webster made "slop" its word of the year in 2025, and LinkedIn now even has a "report AI slop" button to counter the flood of AI generated content that's boomed on the platform.

How much a model can hold in its head at once, measured in tokens. If you go beyond this magical limit, the model starts forgetting what you told it just before. However, it won't tell you when it's reached that point. As technology has improved context windows have grown, but it's worth noting they're readily eaten up, especially in a chat with a lot of files (especially token-hungry ones), and also in chats with a lot of turns.

When an LLM creates an inaccurate, false, or fabricated output. LLMs are prediction machines, trained on a huge body of past data (their corpus). Hallucinations can happen because of gaps or errors in that training data, but more often they're just a side effect of the prediction process itself. It does this confidently (even when wrong), and even when challenged directly it can take several turns of pushing before a model will admit something was fabricated or inaccurate. The major labs claim to have brought hallucination rates down for simple tasks, but it remains a huge challenge for generative AI, which is stochastic by nature.

An AI-powered word predictor. An LLM is a type of AI model trained on huge amounts of text to be able to become a prediction machine for more text based on what you tell and ask it in your prompt and context. LLMs can answer questions, summarise, write code, all based on language. But it is just a predictor and an LLM is not a search engine or database and it recreates patterns it's observed without having any understanding or consciousness of the world around it. In an LLM, words are chunked down into tokens, essentially giving them coordinates in space allowing it to then work out which word is most likely to come next. The model is assembled with statistics and a neural network (a mesh of possibilities) where words and numbers become tokens with weights showing probability of being next. Usually when we refer to "model" in AI, we are actually using shorthand to refer to something bigger than just an LLM. For example, ChatGPT can be powered by a GPT-5.6 Sol model and within the 5.6 Sol model there will be an LLM but also a load of other ingredients that help the whole thing work together as one cohesive system.