Overview
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Build practical skills for designing, grounding, and improving generative AI applications with Google Cloud Vertex AI. In this Specialization, you’ll learn how prompt design, Gemini models, embeddings, vector search, and retrieval-augmented generation (RAG) work together to produce more useful and grounded AI experiences.
You’ll begin with generative AI and large language model fundamentals, then explore prompt engineering and Vertex AI Studio. As you progress, you’ll work with text and multimodal embeddings, grounding techniques, controlled generation, and multimodal Gemini use cases. You’ll then apply these concepts to semantic search, hybrid search, multimodal RAG, and LLM-powered application architectures.
By the end, you’ll be able to:
Design effective prompts for generative AI applications. Ground model responses using retrieval, embeddings, and your own data. Build semantic and hybrid search workflows with Vertex AI Vector Search. Develop multimodal and RAG-based applications using Gemini and Vertex AI.
This Specialization is designed for developers, cloud practitioners, and technical learners who want hands-on experience building generative AI solutions with Google Cloud.
Syllabus
- Course 1: Introduction to Generative AI
- Course 2: Introduction to Large Language Models
- Course 3: Introduction to Vertex AI Studio
- Course 4: Generative AI with Vertex AI: Prompt Design
- Course 5: Grounding Gemini Models in Vertex AI
- Course 6: Create Generative AI Apps on Google Cloud
- Course 7: Multimodal Use Cases with Gemini 1.5
- Course 8: Introduction to Vertex AI Embeddings: Text and Multimodal
- Course 9: Vector Search and Embeddings
- Course 10: Multimodal Retrieval Augmented Generation (RAG) using the Vertex AI Gemini API
Courses
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
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This course introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, prompt engineering, and model tuning. The aim is to enable you to unlock the potential of gen AI in your projects with Vertex AI Studio.
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This is a self-paced lab that takes place in the Google Cloud console. This lab is part of a series designed to provide hands-on experience with Generative AI on Google Cloud.
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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.
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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.
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This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn how to perform multimodal retrieval augmented generation (RAG) using Vertex AI Gemini API.
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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud? In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application.
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This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to use grounding in Vertex AI to generate content grounded in your own documents and data.
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This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will explore the Vertex AI Embeddings API for both Text and Multimodal (Images and Video) use cases.
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This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to use Gemini 1.5 Pro and Gemini 1.5 Flash LLMs for multimodal use cases.
Taught by
Google Cloud Training