Create Embeddings, Vector Search, and RAG with BigQuery

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Create Embeddings, Vector Search, and RAG with BigQuery

Coursera · Intermediate ·🔍 RAG & Vector Search ·3mo ago

Key Takeaways

Creates embeddings, vector search, and RAG with BigQuery to mitigate AI hallucinations

Original Description

This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
Building a Production-Ready RAG Evaluation Framework
Learn to build a production-ready RAG evaluation framework to effectively assess and improve your RAG applications
Dev.to · HIMANSHU KUMAR
📰
Production RAG Is a Different Beast: Guardrails, Evals, and Everything the Demo Doesn’t Show You
Learn the differences between demo and production RAG systems and why guardrails and evaluations are crucial for real-world applications
Medium · LLM
📰
Azure AI Search in 2026, how to build a RAG pipeline
Learn to build a RAG pipeline using Azure AI Search in 2026 and improve your information retrieval capabilities
Dev.to · Carlos José Castro Galante
📰
RAG local en .NET: Chatea con tu Documentación (sin nube, sin API keys)
Learn to implement RAG locally in .NET without relying on cloud services or API keys, enabling you to chat with your documentation
Dev.to AI
Up next
System B: The AIO Discovery Engine Revolution #shorts
josh bachynski
Watch →