Large language models (LLMs) are impressive, but they have a tendency to 'hallucinate'—confidently generating incorrect information. This poses serious risks, especially in areas where accuracy is crucial. But what if we could detect these hallucinations before they cause harm? New research explores a promising approach: analyzing the flow of information *within* the LLM, layer by layer. Instead of just looking at the final output, researchers are investigating how information is processed and transformed as it moves through the model's internal network. This 'layer-wise' analysis reveals that hallucinations often manifest as deficiencies in the information passed between layers. This method, called Layer-wise Information Deficiency (LI), offers a more robust way to gauge an LLM's reliability. By tracking how information gains and losses occur during processing, LI acts as an internal confidence check, flagging potential hallucinations without needing retraining or architectural changes to the LLM. The research dives into scenarios where LLMs grapple with limited or ambiguous information, situations ripe for hallucinations. Early results suggest that LI is a strong indicator of question difficulty and LLM confidence, outperforming traditional methods that focus solely on the final output. This breakthrough opens doors to more trustworthy AI systems by allowing us to identify and potentially correct LLM hallucinations in real-time. The challenge now lies in refining LI and exploring its application across various LLM architectures and real-world scenarios. The goal? An AI that not only generates impressive text but also understands its own limitations.