Large Language Models (LLMs) are changing how we handle data, especially unstructured formats like emails or PDFs. Traditional methods often require expensive custom pipelines or manual effort. Using tools like ChatGPT, a small startup processed a challenging data project in just 2.5 months for $5,000, saving significant time and money.
For example, researchers studying air pollution’s effects on bird populations could use LLMs to clean messy bird-watching reports. These models can extract, standardize, and filter data quickly, even when formats and details vary widely. With proper prompts and fine-tuning, they achieved 95% accuracy, turning chaotic data into a usable format.
While LLMs can make mistakes or “guess” missing data, testing small samples first helps refine results. Despite some challenges, LLMs save time, cut costs, and improve data quality. One company even boosted a key metric by 10% after applying insights from LLM-processed data. How have you seen LLMs improve data workflows? Let’s share ideas!