Large language models (LLMs) are impressive, but they can be overconfident in wrong answers. How can we tell when an LLM is truly confident? New research explores a fascinating approach: examining the *explanations* an LLM generates for its answers. The idea is that a confident LLM should provide consistent, logically sound explanations. Researchers are testing this by prompting LLMs to not just answer questions, but also explain their reasoning. They then evaluate the “stability” of these explanations—how well they logically support the given answer. Initial results show promise, especially for complex questions where deeper reasoning is required. This approach goes beyond simply asking an LLM how confident it is. Instead, it delves into the *why* behind the answer. By analyzing the explanations, we gain insight into the LLM's thought process and can better gauge its true confidence. This research could lead to more reliable and trustworthy AI systems, helping us know when to trust an LLM's answer and when to remain skeptical. It's a step toward making AI not just intelligent, but also self-aware of its limitations.