What LLMs actually are, and why the technical details matter for business users
Most professionals using AI tools in 2026 are working with systems they only partially understand, and that gap has real costs. Knowing what large language models actually do, and where they break down, changes how you use them and how much you trust their output.
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Tokens, training, and inference: what happens when you hit enter
What a model actually does: prediction, not understanding
Hallucinations: why confident answers can be wrong
The context window: the model's working memory
Verifying outputs: trust but check
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The skinny
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