Overview
In this course, you’ll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build engaging and 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.
Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
Skills Covered
- Describe generative-AI-based application types and use cases.
- Describe how to build prompt templates to improve model response quality in applications.
- Describe the subsystems of RAG-capable architectures for generative AI applications on Google Cloud.
- Build an LLM and RAG-based chat application
Prerequisites
Programming experience is recommended. Basic proficiency with command-line tools and Linux operating system environments is helpful. Basic understanding of generative AI is helpful but not required.
Target Audience
- Application developers
- Architects
- Cloud engineers
Course Curriculum
Module 1: Generative AI Applications
This module discusses the use of generative AI in applications, the foundation models provided by Google, and the challenges encountered when building generative AI applications.
Objectives:
- Describe generative-AI-based application types and use cases.
- Describe foundation models provided by Google.
- Discuss challenges of generative AI for applications.
Module 2: Prompts
This module discusses prompts for generative AI models and how to best design prompts to improve effectiveness of model responses.
Objectives:
- Describe how to build prompt templates to improve model response quality in applications.
- Describe the essential and optional components of a prompt.
- Describe how to use a prompt template in an application
Module 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.
Objectives:
Design prompts within Vertex AI Studio
Module 4: Get Started with Vertex AI Studio
This module discusses how foundation model accuracy can be improved with retrieval augmented generation (RAG) and how vector embeddings can be used to find matching data for a user’s query.
Objectives:
- Describe methods for improving foundation model accuracy.
- Describe how vector embeddings can be used to retrieve relevant RAG data for a given query.
- Describe the components of a RAG-capable generative AI application on Google Cloud.
Module 5: Build an LLM and RAG-based 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.
Objectives:
- Build an LLM and RAG-based chat application.
Dates & Locations
Exam & Certification
Note: There is no exam directly associated with this course. However, Google Cloud offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2025 and beyond. Obtaining a Google Cloud certified credential is one of the most effective ways to validate your skills and accelerate your career.
With our expert-led training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
- Prove your expertise with a globally respected credential recognized by employers worldwide.
- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
Explore our full range of Google Cloud certifications and start building the skills that matter today.
