Imagine a world where incomplete data isn't a roadblock to insightful analysis. That's the promise of CLAIM, a groundbreaking method using large language models (LLMs) to fill in missing pieces in datasets. Traditionally, missing data is handled with simple replacements like mean/median or more complex methods like k-NN or MissForest. These methods, however, often struggle with complex datasets and different types of missingness (MCAR, MAR, MNAR). CLAIM takes a different approach. Instead of numerical estimations, it uses LLMs to generate contextually relevant descriptions for missing values. For example, instead of just plugging in an average age, CLAIM might describe a missing age as "likely a young adult" based on other data points. This transforms the dataset into a format LLMs understand better, allowing them to learn from incomplete data more effectively. In tests across various datasets, CLAIM consistently outperformed traditional methods, especially in complex scenarios. It even improved LLM performance on datasets where the LLM struggled with complete data! This suggests that CLAIM not only fills in gaps but also helps LLMs make better sense of the overall data. The research also explored the impact of phrasing missing data descriptions. Turns out, context matters! Specific descriptions like "Malic acid quantity missing for this wine sample" worked better than generic terms like "NaN." This highlights how LLMs leverage their vast language knowledge to interpret and utilize context. While promising, CLAIM has limitations. Its effectiveness depends on the LLM's training data, the quality of context provided, and computational resources. Future research will explore extending CLAIM to other data types like time series and images, incorporating feedback mechanisms, and using domain-specific LLMs. CLAIM represents a significant step towards more robust and reliable data analysis in the age of LLMs.