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Create Embeddings, Vector Search, and RAG with BigQuery

Google Cloud Training

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.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores Retrieval Augmented Generation (RAG), a technique used to improve the accuracy and reliability of generative AI models, which is highly relevant in the field
Uses BigQuery, a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data, which is a valuable skill for professionals
Employs generative AI models like Gemini, which represents a cutting-edge approach to addressing AI hallucination and improving the quality of generated content
Presented by Google Cloud, which is recognized for its contributions to cloud computing and its expertise in data management and artificial intelligence
Teaches how to create embeddings, which are numerical representations of data that capture semantic relationships, and are essential for vector search and RAG
Focuses on mitigating AI hallucinations, a common problem in generative AI, which is a practical and valuable skill for building reliable AI systems

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Reviews summary

Build rag pipelines on google cloud

According to learners, this course provides a practical and timely introduction to building Retrieval Augmented Generation (RAG) pipelines using Google Cloud's BigQuery. Students particularly praise the hands-on labs which help solidify the concepts of embeddings and vector search. While the course effectively covers the implementation details within BigQuery ML, some students felt it could delve deeper into the theoretical aspects or advanced use cases. Overall, it's seen as an excellent starting point for applying RAG techniques, especially for those already working within the GCP ecosystem.
Concepts explained clearly.
"The explanation of the concepts was very clear and easy to understand."
"Lectures were well-structured and explanations were lucid."
"Instructor explained complex topics clearly."
Labs reinforce concepts effectively.
"The labs were particularly useful in solidifying the concepts."
"The hands-on exercises made it easy to follow along and understand the process."
"I found the labs to be very helpful for applying what was taught in the lectures."
Covers current AI/ML techniques.
"Very relevant course on a very current topic (RAG and Embeddings)."
"The topic is very timely and important for anyone working with generative AI."
"Excellent course that covers a very relevant topic in today's AI landscape."
Focuses on building actual solutions.
"Provides a practical example of how to implement RAG in BigQuery. Very useful course."
"I appreciated the focus on the practical application of RAG with BigQuery ML."
"This course helped me understand the implementation of RAG in a real-world scenario using GCP."
"The practical application of concepts using BigQuery was very helpful."
Suggests going beyond basic implementation.
"Could use more in-depth coverage on complex topics or optimization techniques."
"Would have liked to see more discussion on the underlying theory or advanced use cases."
"While a great intro, it feels like it only scratches the surface of RAG implementation."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Create Embeddings, Vector Search, and RAG with BigQuery with these activities:
Review SQL Fundamentals
Refresh your understanding of SQL fundamentals to better grasp BigQuery's SQL dialect used for vector search and data manipulation.
Browse courses on SQL
Show steps
  • Review basic SQL syntax and commands.
  • Practice writing SQL queries on sample datasets.
  • Familiarize yourself with SQL concepts like joins and aggregations.
Brush up on Machine Learning Concepts
Review core machine learning concepts to understand the context of embeddings and generative AI models used in RAG.
Browse courses on Machine Learning
Show steps
  • Review the basics of machine learning algorithms.
  • Understand the concept of vector embeddings and their use in similarity search.
  • Learn about generative AI models and their applications.
Follow BigQuery Tutorials
Work through BigQuery tutorials to gain hands-on experience with the platform and its features.
Show steps
  • Find tutorials on BigQuery's official documentation.
  • Follow tutorials on creating and managing datasets and tables.
  • Practice writing and executing SQL queries in BigQuery.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Simple RAG Pipeline Prototype
Start a small project to implement a basic RAG pipeline using BigQuery and a generative AI model.
Show steps
  • Choose a dataset and a generative AI model.
  • Create embeddings for the dataset using BigQuery ML.
  • Implement a vector search function to retrieve relevant data.
  • Use the generative AI model to generate answers based on the retrieved data.
Document Your RAG Pipeline Journey
Create a blog post or documentation outlining your experience building a RAG pipeline with BigQuery.
Show steps
  • Document the steps you took to build the RAG pipeline.
  • Explain the challenges you faced and how you overcame them.
  • Share your insights and learnings from the project.
Contribute to a RAG-related Open Source Project
Contribute to an open-source project related to RAG or vector search to deepen your understanding and collaborate with others.
Show steps
  • Find an open-source project related to RAG or vector search.
  • Identify an area where you can contribute, such as bug fixes, documentation, or new features.
  • Submit your contributions to the project.
Read 'Natural Language Processing with Transformers'
Read this book to gain a deeper understanding of the Transformer models used in generative AI and embedding creation.
View Natural Language Processing with Transformers,... on Amazon
Show steps
  • Read the chapters related to text embeddings and generative models.
  • Experiment with the code examples provided in the book.
  • Apply the concepts learned to your RAG pipeline project.

Career center

Learners who complete Create Embeddings, Vector Search, and RAG with BigQuery will develop knowledge and skills that may be useful to these careers:
Generative AI Specialist
A Generative AI Specialist focuses on developing and deploying generative AI models, and this course directly enhances your skillset. Core to this course is the exploration of a Retrieval Augmented Generation solution in BigQuery, a critical step in mitigating AI hallucinations. You can leverage the techniques taught to build RAG pipelines using BigQuery and generative AI models like Gemini. The course's emphasis on creating embeddings, searching vector spaces, and generating improved answers directly refines and optimizes your models for real-world applications. This course helps advance your capabilities in ensuring the reliability and accuracy of generative AI outputs.
See salaries and explore the career path for Generative AI Specialist
Natural Language Processing Engineer
Natural Language Processing Engineers specialize in developing systems that understand and generate human language, and this course directly addresses core challenges they face. This course explores a Retrieval Augmented Generation solution using BigQuery, significant for mitigating AI hallucinations. As an engineer, you can use your techniques to build RAG pipelines using BigQuery and generative AI models, thus refining and optimizing your models for real-world applications. By creating embeddings, searching vector spaces, and generating improved answers, the course helps you enhance the accuracy and reliability of NLP systems.
See salaries and explore the career path for Natural Language Processing Engineer
AI Engineer
An AI Engineer develops and implements artificial intelligence solutions, and this course directly addresses key challenges in the field. This course explores a Retrieval Augmented Generation solution in BigQuery, which is vital for mitigating AI hallucinations. As an AI Engineer, understanding how to create embeddings, perform vector search, and generate improved answers using the RAG workflow can be invaluable. You can apply these skills to build robust AI pipelines that leverage tools such as BigQuery and generative AI models like Gemini. The course's practical focus on building RAG pipelines makes it highly relevant for anyone looking to enhance the reliability and performance of AI systems.
See salaries and explore the career path for AI Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for implementing and deploying machine learning models at scale, and this course provides practical knowledge directly applicable to this role. Core to this course is the exploration of a Retrieval Augmented Generation solution using BigQuery, a critical tool for mitigating AI hallucinations. As a Machine Learning Engineer, you can leverage the techniques taught in this course to build RAG pipelines using BigQuery and generative AI models, enhancing the performance and reliability of your machine learning systems. The course's emphasis on creating embeddings, searching vector spaces, and generating improved answers helps you refine and optimize your models for real-world applications.
See salaries and explore the career path for Machine Learning Engineer
Machine Learning Operations Engineer
Machine Learning Operations Engineers streamline the deployment and management of machine learning models, and this course enhances their ability to handle generative AI solutions. This course helps explore a Retrieval Augmented Generation solution in BigQuery, vital for mitigating AI hallucinations. The course explores how to create embeddings, perform vector search, and generate improved answers using the RAG workflow. This knowledge can be invaluable for an engineer, as they implement robust AI pipelines utilizing tools such as BigQuery and generative AI models. The course's practical focus on building RAG pipelines makes it more relevant for improving the reliability and performance of deployed AI systems.
See salaries and explore the career path for Machine Learning Operations Engineer
Data Scientist
A Data Scientist often builds and deploys machine learning models for various applications, and this course equips you with relevant skills. This course introduces the Retrieval Augmented Generation workflow and its implementation using BigQuery, which helps address AI hallucination challenges. You can learn how to create embeddings, search vector spaces, and generate improved answers, all of which are essential for enhancing the accuracy and reliability of AI models you might deploy as a data scientist. This course, which focuses on practical implementation with BigQuery and generative AI models, helps you build a strong foundation for integrating RAG pipelines into your data science projects.
See salaries and explore the career path for Data Scientist
Data Engineer
Data Engineers build and maintain the infrastructure for data pipelines, and this course provides knowledge that directly enhances their capabilities. This course introduces using Retrieval Augmented Generation with BigQuery to mitigate AI hallucinations. As a data engineer, learning how to implement this workflow can prove very useful. You can build RAG pipelines using BigQuery and generative AI models, optimizing data flow and enhancing the accuracy of AI-driven applications. This course focuses on creating embeddings, searching vector spaces, and generating improved answers and helps you design robust and efficient data architectures.
See salaries and explore the career path for Data Engineer
Information Retrieval Specialist
An Information Retrieval Specialist focuses on optimizing search and retrieval processes, and this course directly applies to their work. By exploring a Retrieval Augmented Generation solution in BigQuery, this course will be useful. The course introduces key concepts like creating embeddings, searching vector spaces, and generating improved answers, all crucial for enhancing retrieval accuracy. The practical implementation with BigQuery and generative AI models like Gemini provides hands-on experience that directly improves your capabilities in building efficient and effective information retrieval systems. These skills help ensure that relevant information is retrieved accurately and quickly.
See salaries and explore the career path for Information Retrieval Specialist
AI Product Manager
AI Product Managers oversee the development and launch of AI-driven products, and understanding the technical aspects is crucial for success. By exploring a Retrieval Augmented Generation solution in BigQuery, this course may be useful to the product manager. This course introduces key concepts like creating embeddings, searching vector spaces, and generating improved answers, all essential for mitigating AI hallucinations. As a Product Manager, you can use this knowledge to guide the development of more reliable and effective AI products, ensuring they meet market needs and perform optimally.
See salaries and explore the career path for AI Product Manager
Cloud Solutions Architect
Cloud Solutions Architects design and implement cloud-based solutions, often involving advanced AI and machine learning capabilities. By exploring a Retrieval Augmented Generation solution in BigQuery, this course may be useful. In this course, you can learn how to build RAG pipelines using BigQuery. A Cloud Solutions Architect who understands these techniques can design more effective and reliable AI-driven cloud solutions, helping mitigate AI hallucinations and improve overall system performance.
See salaries and explore the career path for Cloud Solutions Architect
AI Consultant
AI Consultant provides expert advice on implementing AI solutions, and the practical knowledge gained from this course is invaluable. By exploring a Retrieval Augmented Generation solution in BigQuery, this course may be useful for the consultant. The course focuses on mitigating AI hallucinations, which is a common concern for clients. As an AI Consultant, you can use the techniques learned to build and recommend RAG pipelines using BigQuery and generative AI models, helping your clients improve the reliability of their AI systems. Understanding how to create embeddings and search vector spaces enables you to offer well-informed and practical advice.
See salaries and explore the career path for AI Consultant
Big Data Architect
Big Data Architects design and oversee the infrastructure for large-scale data processing, and the capabilities of BigQuery discussed in this course are highly relevant. This course introduces the concept of Retrieval Augmented Generation within BigQuery, to mitigate AI hallucinations. This course may be useful for architects to help conceptualize, implement, and improve AI pipelines with BigQuery and related generative AI models. By learning these techniques, you enhance the capabilities of your big data infrastructure to support advanced AI applications.
See salaries and explore the career path for Big Data Architect
Chief Technology Officer
A Chief Technology Officer (CTO) must stay abreast of advancements in technology. They must also be knowledgeable about new AI developments. This course may be useful to CTOs in order to understand advanced AI systems with BigQuery. The course focuses on mitigating AI hallucinations, which is a common concern for clients. As an CTO, you can use the techniques learned to build and recommend RAG pipelines using BigQuery and generative AI models. The course focuses greatly on practical advice.
See salaries and explore the career path for Chief Technology Officer
Data Analyst
Data Analysts interpret and analyze data to provide insights, and understanding AI-driven solutions can enhance their analytical toolkit. This course introduces Retrieval Augmented Generation in BigQuery, helping to mitigate AI hallucinations. This course may be useful to Data Analysts. You can use the skills learned to better understand and validate AI-generated insights, ensuring the accuracy and reliability of your data-driven recommendations. Knowledge of how to create embeddings and search vector spaces helps you better understand the underlying mechanisms of AI models.
See salaries and explore the career path for Data Analyst
Database Administrator
Database Administrators manage and maintain databases, and with the increasing integration of AI, understanding vector search and embeddings becomes crucial. This course explores Retrieval Augmented Generation solutions in BigQuery. This course may be useful for administrators, to see how AI search is practically implemented via databases and cloud solutions. A DBA can use this understanding to optimize database performance for AI applications and manage the storage and retrieval of embeddings efficiently.
See salaries and explore the career path for Database Administrator

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Create Embeddings, Vector Search, and RAG with BigQuery.
Cover image
Natural Language Processing with Transformers,...
Save
Provides a comprehensive guide to using Transformers for NLP tasks, including text embeddings and generation. It offers a deeper understanding of the models used in RAG pipelines. While not specific to BigQuery, it provides valuable background on the underlying technology. This book is more valuable as additional reading to deepen understanding of the models used in the course.
Natural Language Processing with Transformers,...
Paperback
$$
Natural Language Processing with Transformers,...
Kindle Edition
$$

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Level
Advanced
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
March 17, 2025 - March 31, 2025
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Explores Retrieval Augmented Generation (RAG), a technique used to improve the accuracy and reliability of generative AI models, which is highly relevant in the field
Uses BigQuery, a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data, which is a valuable skill for professionals
Employs generative AI models like Gemini, which represents a cutting-edge approach to addressing AI hallucination and improving the quality of generated content
Presented by Google Cloud, which is recognized for its contributions to cloud computing and its expertise in data management and artificial intelligence
Teaches how to create embeddings, which are numerical representations of data that capture semantic relationships, and are essential for vector search and RAG
Focuses on mitigating AI hallucinations, a common problem in generative AI, which is a practical and valuable skill for building reliable AI systems
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