Unlock the potential of Retrieval-Augmented Generation (RAG) by building intelligent AI agents that leverage large language models (LLMs) to retrieve, process, and generate highly relevant responses. This workshop offers a practical deep dive into RAG-based architectures, equipping attendees with the knowledge and tools to enhance AI-driven applications with external knowledge retrieval and real-time adaptability.

Throughout this hands-on session, participants will design, build, and optimize RAG agents, learning best practices for integrating LLMs with retrieval mechanisms to improve response accuracy and contextual understanding.

Who Should Attend?

This workshop is ideal for:

• Solution Architects
• Developers
• AI/ML Engineers
• Data Scientists

If you're working on LLM-powered applications, conversational AI, or AI-driven search systems, this session will provide valuable techniques for optimizing performance and enhancing knowledge retrieval capabilities.

What to Expect?

• Hands-On Training: Learn how to build and fine-tune RAG-powered AI agents using LLMs.
• Best Practices: Explore architectural considerations, indexing techniques, and retrieval optimization.
• Real-World Use Cases: Apply concepts to build AI-driven assistants, search systems, and contextual response generators.

Important: Participants must bring their own laptops to fully engage in this hands-on session.

Seats are limited to 20 participants. Register early to guarantee your spot!