Large language models (LLMs) are impressive, but they sometimes hallucinate or make things up. One solution is to give them access to external knowledge, but how do we ensure they use this knowledge effectively? Researchers have developed a novel approach called Self-aware Knowledge Retrieval (SeaKR) that dynamically decides *when* LLMs need extra information and *how* to best integrate it. SeaKR works by tapping into the LLM's internal states, essentially allowing the AI to gauge its own uncertainty. If the LLM is unsure about an answer, SeaKR kicks in, retrieving relevant knowledge snippets and intelligently ranking them based on which ones best reduce the AI's uncertainty. Think of it as an AI double-checking its work before giving an answer. For more complex questions requiring multiple steps, SeaKR even helps choose the most effective reasoning strategy. This is a significant step towards more reliable and trustworthy AI systems. By understanding and addressing their own limitations, AI systems can truly unlock their full potential and assist us in a myriad of ways. Further research is needed to refine this self-awareness and apply it to more complex tasks, but the potential of SeaKR is vast and very encouraging.