What it means
Large language models learn patterns from enormous amounts of text. The result is software that can answer questions, summarize, translate, write, classify and reason through problems in natural language. Examples include Claude, GPT and Gemini, along with open-weights models such as Llama, Gemma and Mistral.
Many current LLMs also read images and documents and can call tools, which lets them act as the core of assistants and agents.
Why it matters for a business
LLMs make it practical to automate work that involves reading and writing: emails, documents, support and research. They are not databases of truth, so they work best connected to your own data, with checks for important outputs.
A business example
Things to watch
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Answers can be confidently wrong without grounding.
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Costs grow with input and output length.
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Data terms differ between providers and plans.
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Model versions change, so pin and test.