Build with Vertex AI: Building RAG Applications with Vertex AI

Retrieval Augmented Generation (RAG) allows enterprises to combine Large Language Models (LLMs) with real world data to create grounded applications that are far less likely to produce inaccuracies and hallucinations. This learning path begins with an overview of embeddings, vector space and RAG in BigQuery, and proceeds to equip you with the necessary skills and practical experience to build custom RAG solutions using Vertex AI APIs, vector stores and the LangChain framework.

This path is part of the curriculum to earn the Build with Vertex AI Technical Expert Badge. To earn your Technical Expert Badge you need to have a valid Professional Google Cloud Certification, earn the Skill Badge at the end of this path, and earn the other four Skill Badges to showcase your Build with Vertex AI skills.

You can earn the other Skill Badges needed at Build with Vertex AI: Working with Gemini, Build with Vertex AI: Deploy and Evaluate Model Garden Models, Build with Vertex AI: Generating, Editing, and Responding to Media, and Build with Vertex AI: Deploy Agents with Agent Development Kit (ADK), MCP, and A2A.