Large Language Model (LLM)
An AI model trained on vast amounts of text data that can generate, summarize, translate, and analyze human language. Examples include GPT-4, Claude, Gemini, and Llama.
Why it matters
When you use ChatGPT, Claude, or Gemini, you're talking to a large language model. It matters because these tools now draft emails, explain confusing documents, summarize long reports, and answer questions in plain English. Knowing they predict likely text (rather than looking up facts in a database) explains both why they're so fluent and why they sometimes state wrong things with total confidence.
In practice
Paste a dense three-page insurance letter into an LLM and ask, "Explain this like I'm not a lawyer, and list what I actually need to do." In seconds it turns jargon into a short, plain-English checklist. Different models have different strengths, so it helps to compare a few; aggregators like Vincony let you try many models side by side without separate accounts.
Related terms
Artificial Intelligence (AI)
The simulation of human intelligence by computer systems, including learning, reasoning, and self-correction. Modern AI is primarily powered by machine learning and neural networks.
Context Window
The maximum amount of text (measured in tokens) an AI model can consider at once. Larger context windows allow the model to reference more information in a single conversation.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. Powers chatbots, translation, sentiment analysis, and text summarization.
Prompt
The text instruction you give to an AI model to generate a response. Prompt quality directly impacts output quality — better prompts yield dramatically better results.
Token
The basic unit of text that AI models process — roughly 3/4 of a word in English. 'Unbelievable' is 3 tokens. Token limits determine how much text a model can process at once.
Tokenizer
The component that splits text into tokens (sub-word units) before feeding it to an AI model. Different models use different tokenizers — affecting how they count input length, handle multilingual text, and process code.