Large Language Model
A large language model (LLM) is a neural network trained on a very large amount of text to predict the next token, which lets it write, summarize, translate and answer questions. It is the engine inside chat assistants and most AI agents.
Also known as: LLM, LLMs, large language models
A large language model reads text as tokens and, given everything so far, predicts the next one. Repeat that a few hundred times and you get a paragraph. The “large” refers to the size of the neural network, measured in parameters, and to the amount of text it learns from in pre-training. Post-training then teaches it to follow instructions and hold a conversation.
Because the model is built to produce plausible text, it can be fluent and wrong at the same time, which is why LLMs hallucinate. On its own it also doesn’t know about events after its knowledge cutoff or about your private data. Most useful AI products wrap the model in more: retrieved documents, tools, memory and checks. How large language models actually work walks through the whole process.
Go deeper
- How do large language models actually work? AI, decoded · How Large Language Models Actually Work
- What makes an AI agent different from an LLM? AI, decoded · 4 Things That Turn a Model Into an Agent
- Why do LLMs hallucinate? AI, decoded · Why LLMs Hallucinate
- Should you evaluate AI with an LLM-as-a-judge or with human review? AI, decoded · LLM-as-a-Judge vs. Human Evaluation: When to Use Which
- What is LLM-as-a-judge, and when can you trust it? AI, decoded · What Is LLM-as-a-Judge
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