Why LLMs still confabulate, and what you should actually do about it
Large language models can produce confident, well-formatted, completely wrong answers, and the problem is structural, not a bug waiting for a patch. Understanding why confabulation happens changes how you design workflows, evaluate outputs, and decide when not to use an LLM at all.
Go deeper, free lessons
Hallucinations: why confident answers can be wrong
Hallucinations and verification in high-stakes work
Retrieval-augmented generation (RAG): giving models your data
Verifying outputs: trust but check
What a model actually does: prediction, not understanding
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The skinny
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