Beyond ChatGPT: Making LLMs Work with Real Data using RAG

Large Language Models like ChatGPT are powerful, but they often fall short when it comes to accessing real-time or domain-specific data . This is where Retrieval-Augmented Generation (RAG) comes in, enabling LLMs to combine with external data sources for more accurate, relevant, and grounded responses .

In this session, we will break down RAG in simple terms and explore how modern AI systems use it to power applications such as customer support bots, knowledge assistants, and intelligent search systems . We’ll walk through the end-to-end working of RAG systems and discuss the key components that make them effective in real-world scenarios.

This is a beginner-friendly and practical session designed to help you understand how to connect LLMs with real data.

Key Takeaways:

• What is RAG – understanding how LLMs connect with external data
• Limitations of LLMs – why models struggle with real-time and domain-specific data
• RAG Architecture – key components and end-to-end workflow
• Real-World Applications – use cases like chatbots, search, and knowledge systems
• Practical Understanding – building intuition for real-world RAG systems

Mayank Sultania is an AI Engineer focused on building practical AI solutions for real-world problems. He is passionate about simplifying complex concepts and works across areas like LLMs, voice AI, and intelligent applications.