This description is based on a conversation with 'Sam', which took place over several days in December 2024
AI models rely on a diverse range of data sources during pre-processing to ensure accuracy and contextual relevance. These sources include publicly available text collections like Wikipedia, Common Crawl, and academic papers, often totaling petabytes of data. Additionally, AI systems ingest proprietary datasets, such as licensed news articles, financial reports, and industry-specific databases, which provide specialized knowledge. User-generated content (UGC)—which includes publicly available forum discussions, blog comments, and some social media posts—also plays a role, though its use is limited by data privacy policies. Social media data is often sourced from platforms like Reddit, X (formerly Twitter), and publicly accessible parts of Facebook or LinkedIn, with strict filtering to remove personally identifiable information, misinformation, or low-quality content.
The LLM pre-processing stage also includes structured data sources, like enterprise databases, government records, and sensor data. Like scientific research papers, these sources provide high-quality, numerical, and categorical information that enhances AI predictions. Multimodal datasets, combining text, images, video, and audio, are increasingly used to train models with broader contextual awareness—especially for generative AI applications. For example, OpenAI’s GPT models leverage a mix of public text, licensed proprietary data from publishers and research institutions, and synthetic data to refine responses. The careful selection and cleaning of these sources ensures that AI systems operate efficiently while maintaining factual accuracy and ethical considerations.