Verified75% confidenceFactExact time
Prompt engineering techniques such as chain-of-thought, few-shot prompting, and self-consistency cannot enable a model to access information beyond its training cutoff date, cannot guarantee the precision of floating-point arithmetic, and cannot handle external files the model has never seen.
3
Sources
75%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Related Claims
UnverifiedModels have no confidence-gating mechanism during generation and cannot determine whether they know something; when faced with a knowledge gap, they do not stop to express uncertainty but instead continue generating the statistically most fluent continuation.78% similarUnverifiedThe improvement ceiling of runtime improvement is constrained by the base model's inherent capabilities, and the model cannot break through the knowledge and reasoning boundaries obtained during pre-training through runtime optimization.77% similarVerifiedThe core capabilities of large language models are reasoning and question answering, but they lack memory and their knowledge has a temporal cutoff at the time of training.77% similarVerifiedModels lacking self-verification tend to fall into a cycle of confident errors, making it difficult to reliably complete multi-step end-to-end tasks even when initial generation accuracy is relatively high.77% similar
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