Large Language Model
A neural network trained on vast text data to understand and generate human-like language across many tasks.
In plain English
A large language model is an AI trained on huge amounts of text so it can read your question and write a useful answer. It powers chatbots and writing assistants.
Technical definition
A large language model is a transformer-based neural network with billions of parameters trained on extensive text corpora using self-supervised next-token prediction. It generates language by autoregressively predicting tokens conditioned on the preceding context.
Business use case
Businesses integrate LLMs to power chatbots, draft and summarize documents, extract data from unstructured text, and assist with coding. They reduce manual effort on language-heavy tasks and enable self-service support at scale.
Example
A customer pastes a long product review into an assistant, and the LLM instantly returns a one-sentence summary plus the overall sentiment.
Frequently asked questions
A large language model is an AI system trained on enormous amounts of text so it can understand prompts and generate coherent, human-like language for tasks like answering questions, writing, and summarizing.
LLMs use a transformer architecture to predict the next token in a sequence based on patterns learned during training, generating responses one token at a time.
No. LLMs can produce fluent but incorrect answers, known as hallucinations, so their outputs should be verified for important decisions.
Keep exploring
Generative AI
Generative AI is technology that makes brand-new content, like writing, pictures, or code, instead of just sorting or labeling existing data. You describe what you want, and it produces something original.
Natural Language Processing
Natural language processing is the part of AI that helps computers understand and use human language. It is what lets software read your text, translate it, or figure out how you feel.
Token
A token is a small piece of text, like a word or part of a word, that an AI reads one at a time. Models count tokens to measure how much text they can handle.
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