In this module, you learn about generative AI applications, and types of applications that can use the power of generative AI. You also learn about foundation models provided by Google, and challenges that exist when using generative AI in your applications.

Getting started

Introduction to generative AI applications

Foundation models

Challenges of Gen AI for applications

Summary

Generative AI Applications Question 1

Generative AI Applications Question 2

Generative AI Applications Question 3

Generative AI Applications Question 4

Generative AI Applications Question 5

Generative AI Applications Question 6

Generative AI Applications Question 7

Generative AI Applications Question 8

Generative AI Applications

In this module, you learn about generative AI prompts. A prompt is a natural language request submied to a language model to request a response back. You can design your prompts to improve the results being returned from a model.

Introduction to prompts

Prompt structure

Prompt engineering

Prompts Question 1

Prompts Question 2

Prompts Question 3

Prompts Question 4

Prompts Question 5

Prompts Question 6

Prompts Question 7

Prompts Question 8

Prompts

In this module, you experiment with Vertex AI Studio, which provides tools to rapidly prototype, tune models with your own data, and seamlessly deploy to applications. You explore multimodal capabilities of Gemini, design prompts, and generate conversations.

Get Started with Agent Studio

Get Started with Vertex AI Studio

In this module, you learn how to improve the accuracy of foundation models. You learn about retrieval augmented generation, or RAG, a technique for grounding a foundation model with external sources of knowledge. You'll also see an example RAG-capable generative AI solution architecture on Google Cloud.

Improving foundation model accuracy

Retrieval Augmented Generation (RAG)

Retrieving relevant data

RAG architecture using Vertex AI

Retrieval Augmented Generation (RAG) Question 1

Retrieval Augmented Generation (RAG) Question 2

Retrieval Augmented Generation (RAG) Question 3

Retrieval Augmented Generation (RAG) Question 4

Retrieval Augmented Generation (RAG) Question 5

Retrieval Augmented Generation (RAG) Question 6

Retrieval Augmented Generation (RAG) Question 7

Retrieval Augmented Generation (RAG) Question 8

In this module, you build a chat application that uses large language models (LLMs) and retrieval augmented generation (RAG) to create engaging and informative conversations.

Build an LLM and RAG-based Chat Application with AlloyDB and Agent Platform

Build an LLM and RAG-based Chat Application

Course resources

Course Resources

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. Explore other content in the Gemini Enterprise Agent Ready (GEAR) program.

Create Generative AI Apps on Google Cloud

Learn about Gen AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. 

Build and Modernize Applications With Generative AI

Professional Machine Learning Engineer with Google Cloud

Retrieval Augmented Generation (RAG) Question 6

Create Generative AI Apps on Google Cloud

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