学完这些课程后，我对创建专业的可视化图表和仪表板充满信心
Shoebur Rahman采购专员，Giz Bangladesh
课程简介
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.
先修要求
本课程没有先修要求
课程体系
课程大纲
1
Generative AI Applications
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 started50 XP
- Introduction to generative AI applications50 XP
- Foundation models50 XP
- Challenges of Gen AI for applications50 XP
- Summary50 XP
- Generative AI Applications Question 150 XP
- Generative AI Applications Question 250 XP
- Generative AI Applications Question 350 XP
- Generative AI Applications Question 450 XP
- Generative AI Applications Question 550 XP
- Generative AI Applications Question 650 XP
- Generative AI Applications Question 750 XP
- Generative AI Applications Question 850 XP
2
Prompts
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.
3
Get Started with Vertex AI Studio
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.
4
Retrieval Augmented Generation (RAG)
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.
5
Build an LLM and RAG-based Chat Application
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.
6
Course Resources
Link to lesson PDFs
R
Create Generative AI Apps on Google Cloud
课程已完成
