Large language models (LLMs) are impressive, but they sometimes 'hallucinate,' meaning they generate incorrect or nonsensical information. This is a significant challenge, especially when dealing with structured data like product catalogs or databases, where accuracy is paramount. Imagine an AI generating a product description that claims a cotton shirt is made of plastic—not ideal! Researchers are tackling this problem head-on. A new paper introduces 'Confidence-Aware Sub-structure Beam Search' (CABS), a technique to make LLMs more truthful. Instead of looking at the entire output at once, CABS breaks it down into smaller parts (sub-structures), like individual product attributes. It then uses a 'Confidence Network' to evaluate how sure the LLM is about each part. If the AI isn't confident about a detail, CABS revises the prompt, guiding the LLM towards a more accurate answer. This approach is like double-checking your work. By focusing on the AI's confidence at a granular level, CABS helps prevent errors from snowballing into larger hallucinations. The results are promising: CABS significantly outperforms traditional methods, generating product attributes with greater accuracy. This research is a step towards more reliable AI, paving the way for LLMs to be trusted with critical tasks in various industries. While challenges remain, techniques like CABS offer hope for taming AI hallucinations and unlocking the full potential of large language models.