Unverified60% confidenceFactExact time
When constraining output format through prompts, the model may forget these constraints in long conversations or complex reasoning scenarios.
2
Sources
60%
Confidence
Long-term
Relevance
4/12/2026
First Seen
Sources
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Related Claims
UnverifiedAligned models tend to proactively introduce disclaimers or interrupt conversation flow when handling hypothetical reasoning and role-playing tasks, behaviors that are almost nonexistent in base models.80% similarVerifiedReasoning models have built-in autonomous decomposition and verification mechanisms, and overly detailed prompts can actually compress the model's reasoning space.79% similarUnverifiedPrompting provides instructions and examples at inference time, has the lowest cost and fastest iteration, and is suitable for solving problems such as expression style, format constraints, and task boundary specification, but it does not change model weights, so it cannot introduce private knowledge the model does not know, nor can it guarantee real-time capability.78% similarUnverifiedThe context boundary refers to all the input visible to the model in a single inference; if not explicitly constrained, the system prompt, conversation history, and irrelevant retrieved fragments get spliced into the context, and small models are easily distracted into hallucinating.78% similarUnverifiedWhen a prompt contains a clear persona, tone, target reader, structural requirements and specific examples, the model's output space narrows and generation quality jumps.78% similar
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