RAG systems, which integrate language generation and information retrieval, are pivotal in applications like chatbots. The new iRAG model proposes an incremental approach to handle large-scale multimodal data efficiently. This system dynamically updates to handle real-time user queries by selectively mining information from vast data sets. Major advantages include:

• Handling large-scale data dynamically. Adjusts in real-time to user input minimizing data preprocessing needs.
• Real-time content updates. Offers continuous refinement of the retrieved data based on ongoing user interactions.
• Minimized upfront loading times. Reduces the initial data load, distributing processing over time and as needed.
• Enhanced query-specific extraction. Improves the relevance of returned information by focusing on current interaction context.

The iRAG system is valuable for applications requiring real-time data processing from broad information repositories, such as in medical diagnostics or customer service automation. This approach not only speeds up information retrieval but also enhances data relevance and interaction quality, pushing boundaries in AI-driven contextual understanding.