Large Language Models (LLMs) have a reputation for being computationally expensive, especially when they need to retrieve information from external sources. But what if LLMs could get smarter about *when* they need extra help? New research explores training LLMs to predict their own knowledge gaps, effectively reducing reliance on resource-intensive retrieval processes. This "I Know" (IK) score allows an LLM to judge whether the answer already resides within its internal memory or if a retrieval trip is necessary. The results are promising: Experiments showed a substantial decrease—over 50%—in retrieval steps for certain tasks, like question answering. This means faster response times and lower computational costs. The key lies in training the LLM to predict its accuracy by using another LLM, acting as a judge, to evaluate the generated answers. Adding just a small snippet of the generated answer into the LLM's input dramatically improves the IK score’s effectiveness, helping the model make a better decision on whether to retrieve or not. Even more exciting, this technique doesn't require mountains of training data. A relatively small dataset can be enough to achieve reasonable IK prediction accuracy. While the current accuracy of IK scores sits around 80%, even this level of certainty yields significant efficiency gains. Future research could refine the IK training process, improving accuracy and further minimizing the need for external retrieval. This work suggests a compelling path towards more efficient and cost-effective LLM operation, opening doors for wider adoption and applications of these powerful AI models.