Explore our latest guides and tutorials on using large language models effectively.
Most people start using large language models the same way. They type a quick question, read the response, and move on. Sometimes the answer is...
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Every large language model generates incorrect information sometimes. These errors, called hallucinations, range from minor factual slips to entirely fabricated sources, statistics, and events. The...
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You ask a clear question and get a confident, detailed answer. The formatting looks professional, the tone sounds authoritative, and every sentence reads like it...
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Large language models can write emails, analyze documents, generate code, and answer complex questions. They can also waste your time, produce wrong answers, and cost...
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Dozens of large language models are available today, and each one handles different tasks at different price points. Picking the wrong model wastes money on...
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Every time you send a prompt to an LLM, a set of hidden controls shapes the response. These settings determine whether the output sounds creative...
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Every prompt you send to an LLM makes an implicit choice. You either let the model figure out what you want from your instructions alone,...
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Every interaction with a large language model starts with a prompt. The words you type shape the response you receive, and small changes in phrasing...
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The difference between a useful LLM response and a frustrating one often comes down to how you ask. A vague prompt returns vague output. A...
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Every large language model costs money to run. Whether you are chatting through a free web interface or sending thousands of API calls, someone is...
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