Can AI reason like us? Recent research suggests that today’s most advanced AI models, despite their impressive language skills, struggle with even basic planning tasks. A new study challenges the effectiveness of a popular technique called "ReAct prompting," which was believed to enhance the planning abilities of Large Language Models (LLMs). The research, focusing on a simulated household environment, reveals that ReAct's success isn't from improved reasoning, but rather from the AI simply mimicking examples it's been given. When the task deviates even slightly from these examples, the AI's performance plummets. This suggests that LLMs aren't truly reasoning about the problem, but instead relying on superficial pattern matching. This discovery has significant implications for how we design and prompt LLMs. Instead of focusing on complex prompting techniques like ReAct, the research suggests we should explore alternative approaches that encourage genuine reasoning and problem-solving. This might involve incorporating external knowledge sources, improving the models' understanding of cause and effect, or developing new training methods that go beyond simple pattern recognition. The quest for truly intelligent AI continues, and this research provides valuable insights into the challenges we face and the paths we might take to overcome them.