Retrieval-Augmented Generation (RAG) enhances large language models by retrieving and grounding answers in external data sources, making responses more accurate and trustworthy. Grounding ensures outputs are tied to verifiable facts, helping you build AI solutions tailored to unique datasets.

Google Cloud’s Vertex AI simplifies RAG by combining vector search, data pipelines, and foundation models on a secure, scalable platform. With Vertex AI, you can quickly embed custom knowledge into AI systems and deploy them to production.

In this session, participants will build their own RAG-powered AI in Vertex AI with the help of Google Colab (Jupyter Notebooks). Specifically, we will walk through the steps with the workshop content at https://github.com/GoogleCloudPlatform/generative-ai/tree/main/rag-grounding

Participants will leave with practical experience, a working AI in Vertex AI, and the skills to create grounded, reliable AI for their projects.