Coursera MOOC / Non-credit 0

Create Embeddings, Vector Search, and RAG with BigQuery

Name: Create Embeddings, Vector Search, and RAG with BigQuery
Price: 49 USD
Availability: InStock

About this course

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.

67/100

CourseAsk score

What the provider tells you: 31/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

Create embeddings for text data using BigQuery and embedding models
Perform vector similarity searches in BigQuery vector spaces
Build end-to-end RAG pipelines to mitigate AI hallucinations
Integrate generative AI models like Gemini with BigQuery workflows
Apply RAG techniques to real-world use cases requiring accurate AI outputs

Course objectives

Understand the conceptual foundations of Retrieval Augmented Generation
Implement RAG workflows using BigQuery's vector search capabilities
Learn to address AI hallucination problems with practical solutions

Artificial Intelligence Cloud Computing #google cloud #generative ai #large language models #gcp #natural language processing #gemini #retrieval-augmented generation #rag #embeddings #vector search #llm #bigquery #vector databases #semantic search #ai hallucination

$49.00

Price shown by Coursera — confirm on their site.

Enroll on Coursera

You'll be redirected to Coursera to complete enrollment.