Imagine a world where language barriers are effortlessly bridged by AI-powered dictionaries that not only offer accurate translations but also illustrate word usage with perfect bilingual examples. This isn't science fiction, but the focus of recent research exploring how Large Language Models (LLMs) can revolutionize lexicography—the art and science of dictionary-making. Researchers put LLMs to the test, generating example sentences for English paired with French (high-resource), Indonesian (mid-resource), and Tetun (low-resource). The goal? To create examples that are not only accurate translations but also exemplify the nuances of each word's meaning and usage. The team evaluated these AI-generated examples using the "GDEX" criteria: typicality (does it show common usage?), informativeness (does it add clarity beyond the definition?), and intelligibility (is it easy to understand?). The results were promising, with LLMs showing real potential for creating helpful bilingual examples, particularly for high-resource languages like French. However, performance dipped with lower-resourced languages like Tetun, highlighting the ongoing challenge of sufficient training data. Interestingly, human preferences for example quality varied significantly, underlining the subjective nature of language and the challenge of creating examples that resonate with everyone. To address this, the researchers employed "in-context learning" to fine-tune the LLM to individual preferences, successfully aligning the AI's output with human judgment. Beyond generation, the team explored whether LLMs could also *rate* example quality. Using metrics like sentence perplexity (a measure of how well a sentence fits the language model), they found that LLMs could indeed predict how humans might perceive typicality and intelligibility, particularly for high-resource languages. This opens exciting possibilities for automated quality control and self-improving dictionary creation. The study's findings offer a glimpse into a future where AI collaborates with humans to break down language barriers. While challenges remain, especially for low-resource languages, the potential of LLMs to create dynamic, personalized, and highly effective bilingual dictionaries is clear. This research not only advances the field of computational linguistics but also brings us closer to a world where communication flows seamlessly across languages.