Large language models (LLMs) are impressive, but they can sometimes generate incorrect or nonsensical responses—a problem known as 'hallucination.' How can we ensure that the information LLMs give us is accurate, particularly for complex, open-ended tasks? Researchers have developed a new method called CheckEmbed, a powerful technique that verifies the solutions LLMs provide. CheckEmbed works by comparing the embeddings of different LLM answers (or sections of answers) to each other and to a potential ground truth, if available. An embedding is a mathematical representation of text, capturing its meaning in a way that computers can understand. Think of it like comparing fingerprints: Similar texts will have similar embeddings. CheckEmbed leverages the fact that modern embedding models are highly sophisticated, reflecting the nuances of meaning within text. By comparing these embeddings, CheckEmbed can quickly and accurately assess the similarity of LLM-generated responses. This allows it to gauge an LLM's confidence in its answer—repeatedly similar answers imply high confidence, while variations suggest uncertainty. This technique goes beyond existing methods that focus on individual words or sentences, offering a more holistic approach to evaluating complex LLM outputs. Researchers built a complete verification pipeline around CheckEmbed, incorporating helpful assessment metrics. One such metric is an embedding heatmap, visualizing the similarity between all generated answers and the ground truth. The pipeline also uses statistical summaries to provide thresholds for decision-making. These thresholds enable practical applications to determine whether to accept an LLM's response or prompt it to regenerate. Tested on real-world document analysis tasks like term extraction and summarization, CheckEmbed demonstrated significant improvements in accuracy, cost-effectiveness, and runtime compared to existing techniques. Its speed and simplicity stem from the fact that it only needs to embed the full answers and compare them using measures like cosine similarity. This approach holds great promise for ensuring reliable outputs from LLMs, opening doors for broader and more trustworthy AI applications in various fields.