Plain-language explainer
What LLMs can't do
What are the real limits of large language models?
The honest list: they cannot reliably know what they don't know, so confidence and correctness come apart. They have no memory beyond the context window unless a product bolts one on. Exact arithmetic and counting are unreliable without a calculator tool. They cannot check facts against the world, only against patterns in training text. And they cannot act, browse or run code by themselves; every 'agent' is a harness of tools and permissions wrapped around the same predictor.
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What people get wrong
- The limits mean LLMs are unreliable at everything. With tools, retrieval and checks, they are dependable inside well-defined lanes.
- Scale will erase all of this. Some limits, like calibrated self-knowledge and grounding, are structural to prediction and have survived every scale-up so far.
- A wrong answer proves the technology is fake. Same system, different task fit. Knowing which tasks fit is the actual skill.
Where you see it in real products
- Chatbots ship with calculators and code interpreters because raw arithmetic is a known weak spot.
- Agent products ask permission before acting: the harness, not the model, is the safety layer.
- The 'AI can make mistakes' disclaimer under every chat box is this page, in one line.
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