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.
Create Embeddings, Vector Search, and RAG with BigQuery
Create Embeddings, Vector Search, and RAG with BigQuery
This course is part of Gemini in BigQuery Specialization
Instructor: Google Cloud Training
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Advanced level
Designed for those already in the industry
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Generate embeddings using the embedding models with BigQuery.
Perform vector search in BigQuery and understand its process.
Create a RAG (Retrieval Augmented Generation) pipeline with BigQuery.
Skills you'll gain
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Assessments
1 assignment
Taught in English
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This course is part of the Gemini in BigQuery Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There is 1 module in 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.
What's included
6 videos1 reading1 assignment1 app item
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