Retrieval Augmented Generation (RAG) is a technology that combines the benefits of semantic search and large language model-based text generation.

The generative AI space has been rapidly evolving. While we are in a phase of rapid experimentation and PoCs, 2025 is pegged to be the year of generative AI driving real business value. With the growing demands of complex use cases on AI systems, the need for more context-aware models has been rising. Retrieval Augmented Generation, or RAG, is a novel technique that enhances the natural language abilities of Large Language Models by integrating external knowledge retrieval with generation.

This introductory talk is aimed at explaining why RAG is becoming a critical component of the AI toolkit. If you want to build AI systems or are simply interested in the latest trends in generative AI, this session is designed to provide an introduction, insights and practical knowledge on RAG.

The session will include:

• LLMs and the need for Retrieval Augmented Generation : The limitations of current models and why RAG is crucial in overcoming them
• What is RAG and how does it help? The definition of RAG, how it works and what are its advantages
• Real-world applications: Some examples of RAG in action across industries
• Building a RAG system: The basics of how to design and implement RAG systems.
• Challenges and Emerging Patterns: Challenges in deploying RAG systems and the trends that are shaping the field.

Date of the Webinar:
September 12 - 6:00 PM

Registration is required for participation!