Large language models
Large language models are AI systems trained on vast amounts of text to learn patterns in language. They can generate and transform text—such as answering questions, summarizing documents, translating, or drafting content—by predicting the most likely next words given the context. Many LLMs are based on transformer neu
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What are large language models (LLMs)?
Large language models are AI systems trained on vast amounts of text to learn patterns in language. They can generate and transform text—such as answering questions, summarizing documents, translating, or drafting content—by predicting the most likely next words given the context. Many LLMs are based on transformer neural network architectures and are trained using self-supervised learning (e.g., predicting missing or next tokens).
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What they can and can’t do
LLMs are strong at language tasks because they capture statistical relationships between words and concepts. However, they do not “understand” in the human sense, and they can produce incorrect or misleading information (often called hallucinations). Their outputs depend heavily on the quality of the prompt and the data they were trained on, and they may reflect biases present in training data. For high-stakes decisions, it’s important to verify claims with reliable sources and use domain expertise.
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How they’re used and how to use them safely
In practice, LLMs are used for customer support, search assistance, writing assistance, coding help, and educational tutoring. To improve reliability, users can provide clear instructions, relevant context, and constraints (e.g., desired format). Avoid sharing sensitive personal data. Treat outputs as drafts or suggestions rather than authoritative facts, especially for medical, legal, or financial matters.
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