Connecting AI agents to the internet or live data sources often leads to a critical issue: data loss. When utilizing scrapers to retrieve live content from the internet for AI applications, the integrity and accuracy of the data are compromised. This is primarily because the AI cannot perceive visual content such as images, graphs, and charts present in the source. This paper introduces a novel method for multi-modal data extraction and processing, enabling deep training of AI agents and models to enhance their accuracy.

We are in the midst of a race of who can scrape most of the internet and put it on their servers in the least timeframe. Yes, that’s how all the language models are conventionally built. However, this process is slow, this process is old, this process hallucinates, and doesn’t know what is happening in the real-world. And that’s why this convectional process needs a replacement. Real-Time Retrieval Argumentation is the alternate architecture for LLMs that the new age models need to adapt. With RTRA, a model trained on just 7 billion parameters can beat models trained on hundreds of billions of parameters. This research paper marks a paradigm shift by challenging our dependency on huge computational resources to build a precise and efficient model by introducing a novel approach to train and develop language models.