Learn · AI fundamentals
Multimodal AI handles more than text. A multimodal model can read images, listen to audio, watch video, and respond across those formats, not just words. As of 2026 the leading models are multimodal by default, so you can show one a photo, a chart, or a screenshot and ask about it.
This unlocks practical use cases: describing an image for accessibility, pulling data out of a scanned invoice, answering questions about a diagram, or generating images from a text prompt. The model connects meaning across formats instead of treating each in isolation.
For a business, multimodal means AI can work with the messy real-world inputs you actually have, like photos, PDFs, and recordings, rather than only clean typed text.
Updated July 2026
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It depends on the job. As of July 2026 the main families are OpenAI's GPT-5 models, Anthropic's Claude models, and Google's Gemini models, each with a small, fast, cheap tier and a larger, more capable one. Match the tier to the task instead of always reaching for the most powerful.
A large language model (LLM) is an AI system trained on huge amounts of text to predict what words come next. That simple mechanism, scaled up, lets it write, summarize, translate, answer questions, and reason through problems. ChatGPT, Claude, and Gemini are all interfaces to LLMs.
An AI model breaks your input into tokens (word pieces), then predicts the most likely next token, adds it, and repeats, one token at a time, until the answer is complete. Every word it produces is a probability-weighted guess informed by patterns it learned during training.
Tokens are the chunks of text an AI model reads and writes. A token is roughly three-quarters of a word in English, so 1,000 tokens is about 750 words. They matter because AI pricing and limits are measured in tokens, not words or characters.
A context window is the maximum amount of text an AI model can hold in mind at once, counted in tokens. It includes your prompt, any documents you attach, the conversation so far, and the answer being written. Once you exceed it, the model loses track of the earliest content.
AI models hallucinate because they generate the most plausible-sounding next words, not verified facts. When they don't know something, they don't stay silent. They produce a confident guess that fits the pattern of a real answer, which is why a made-up citation or statistic can look entirely legitimate.