This new, dead simple prompt technique boosts accuracy on LLMs by up to 76% on non-reasoning tasks

In the chaotic world of Large Language Model (LLM) optimization, engineers have spent the last few years developing increasingly esoteric rituals to get better answers.

We’ve seen "Chain of Thought" (asking the model to think step-by-step and often, show those "reasoning traces" to the user), "Emotional Blackmail" (telling the model its career depends on the answer, or that it is being accused of sexual misconduct), and complex multi-shot prompting frameworks.

But a new paper released by Google Research suggests that we may have been overthinking it. The researchers found that simply repeating the input query—literally copying and pasting the prompt so it appears twice—consistently improves performance across major models including Gemini, GPT-4o, Claude, and DeepSeek.

The paper, titled "Prompt Repetition Improves Non-Reasoning LLMs," released last month just before the holidays, presents a finding that is almost suspiciously simple: for tasks that don’t require complex reasoning steps ...