Inside this session

-The Foundations
What is RAG, really? — We start with the basics: what large language models are actually doing under the hood, and why Retrieval-Augmented Generation exists in the first place. You'll see a side-by-side of a traditional LLM response vs. a RAG-powered one — same question, dramatically different reliability.

-Why It Matters
From hallucination to grounding — Explore where RAG shows up in the real world: context grounding for enterprise-safe answers, and how UiPath's GenAI Activities put this directly into your automation workflows.

⚙️ Core Components of RAG + UiPath
The engine room — We break down the four building blocks that make RAG work in production:

• Knowledge Source — where your truth actually lives

• Vector Database — how information gets stored for meaning, not just keywords

• Embedding Model — the translator between language and math

• Prompt Construction & Re-engineering — how the right context turns into the right answer

🚀 RAG in Action — Live Demo
Theory's great. Let's build. — See it all come together with:

• UiPath Autopilot in action

• RAG inside an agentic workflow — retrieval, grounding, and decision-making working as one system

Come with your questions — whether you're curious about hallucination control, vector DB choices, or how to wire this into your own agentic builds, this is the session to bring it up.