Google Research revealed a surprisingly simple technique that dramatically boosts LLM accuracy. Repeating the input query can improve performance by up to 76%. The paper, released last month, challenges complex prompting methods.

Researchers discovered that duplicating prompts enhances results for tasks not requiring intricate reasoning. The technique works across major models like Gemini, GPT-4o, Claude, and DeepSeek. Carl Franzen reported the findings on VentureBeat, January 13, 2026.

This discovery could simplify AI development and reduce reliance on complex prompting strategies. Initial reactions suggest widespread adoption due to its ease of implementation. The AI community is now evaluating the technique's limitations and potential applications.

For years, engineers have developed increasingly complex prompting methods. Techniques like "Chain of Thought" and "Emotional Blackmail" aimed to improve LLM responses. This new research suggests a more direct approach can be equally, if not more, effective.

Future research will likely explore the underlying mechanisms behind this phenomenon. Scientists will also investigate its effectiveness across a broader range of tasks and models. The focus now shifts to understanding why such a simple method yields such significant improvements.