From training LLMs to getting real-time data for custom GPTs and RAG, everyone is turning to scraping: Here’s why

In artificial intelligence (AI), it’s clear that data is critical. The growing interest in Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) and RAG (Retrieval-Augmented Generation) emphasizes the crucial role of vast and diverse datasets in training these powerful AI models. As these models become more complex and capable, the need for fresh, varied, and real-time data increases significantly. This is where web scraping comes in. It has become essential for collecting the vast amount of data needed for AI development, especially in training LLMs and customizing GPTs and RAG models.

GPT-3 and similar large language models have revolutionized what machines can do with language. They can write coherent and contextually relevant text and even generate programming code. These models learn from many text data, finding patterns and making connections between words, phrases, and ideas. The only issue is that they need a lot of data. The more varied and extensive the dataset, the more detailed and accurate the model’s output. This need has increased interest in web scraping as an efficient way to gather data from the vast and constantly changing internet.