Google Cloud Generative AI Leader

Course overview

Many executives and team leads are asked how their organisation should use generative AI, but the information they receive is often scattered: some of it is so technical it is hard to follow, and some is so driven by hype that it is hard to act on. Leaders therefore need enough understanding of gen AI fundamentals to talk with technical teams, know which Google Cloud tools suit which kinds of work, and know how to choose use cases, measure results and manage risk so that AI investment delivers real value.

This course covers the skills measured in the Google Cloud Certified Generative AI Leader exam, following the latest outline on the Google Cloud website, across all four sections: gen AI fundamentals, Google Cloud's gen AI offerings, techniques to improve model output, and business strategies for success. Learners work through business examples, demonstrations and hands-on trials of tools such as Gemini, Google AI Studio and NotebookLM with their own accounts, without writing code, before a closing workshop that plans a gen AI use case for their organisation. The course helps with exam preparation, but learners book the exam with Google Cloud themselves. (1 day, 6 hours, Beginner level.)

What you’ll gain

Explain the core concepts of generative AI, foundation models and machine learning
Assess the quality and readiness of data for AI work in the organisation
Choose a Google foundation model that fits a business use case
Distinguish Google Cloud's gen AI offerings, from ready-made tools to developer platforms
Explain the role of agents and the tools they use to get work done
Use prompt engineering, grounding and RAG to improve model output
Plan gen AI adoption with clear measures of success, covering secure AI and responsible AI
Review the skills in the Generative AI Leader exam outline and plan exam preparation

Who this course is for

Executives, team leads and project managers who make decisions about generative AI
Business, product, sales and pre-sales teams who discuss AI with customers or technical teams
Digital transformation and innovation teams who choose use cases and tools
Anyone preparing for the Google Cloud Certified Generative AI Leader exam

Prerequisites

No programming or machine learning background is needed
Some experience with AI tools such as Gemini or other chatbots will help you move faster
A problem or work process in your organisation where you would like to apply AI, for the workshop
A laptop with a browser and a Google account for trying the tools
A Google Cloud account is not required; learners may follow the console demonstrations with their own free trial if they wish

Curriculum

Course Details

This course covers the skills measured in the Google Cloud Certified Generative AI Leader exam, following the latest outline on the Google Cloud website, across all four sections. It runs for 1 day, 6 hours (09:00-16:00), as lectures, demonstrations and workshops. Beginner level, with no coding. It differs from Data & AI for Business Leaders, which focuses on data strategy in general, and from Google Gemini for Work, which focuses on everyday use, by giving leaders an overview of Google Cloud's gen AI offerings. Learners try tools such as Gemini, Google AI Studio and

NotebookLM with their own Google accounts; Gemini Enterprise and Gemini for Google Workspace need an organisational licence, so they are demonstrated. Anyone who wants to try the Google Cloud console uses their own free trial or project at their own cost. The course helps with exam preparation but is not official Google Cloud courseware; it does not include the exam fee or a voucher, and learners book the exam themselves. Learners take home a summary of Google Cloud's gen AI offerings, a workshop template for planning use cases and a summary of the skills in the exam outline.

Day 1: Strategic Generative AI on Google Cloud

Section 1 Generative AI Fundamentals and the Exam Outline

The four exam sections: fundamentals (~30%), Google Cloud's offerings (~35%), improving output (~20%) and business strategy (~15%)
AI, machine learning, NLP, foundation models, LLMs, multimodal models and diffusion models
How supervised, unsupervised and reinforcement learning differ
The machine learning lifecycle from data ingestion to model management, and the Google Cloud tools for each stage

Section 2 Workshop: Data and Generative AI

Data quality: completeness, consistency, relevance and accessibility
Structured and unstructured data, labelled and unlabelled data
The business implications of different data types
Workshop: assess data readiness for an organisational use case

Section 3 Lab: The Gen AI Landscape and Google's Models

The five layers: infrastructure, models, platforms, agents and applications
Strengths and use cases of Gemini, Gemma, Imagen and Veo
Criteria for choosing a foundation model: modality, context window, security, cost and customisation
Lab: try text and image prompts in Google AI Studio

Section 4 Google Cloud's Strengths in Gen AI

An AI-first approach and an enterprise platform that is secure, private and scalable
AI Hypercomputer, TPUs, GPUs and Google's data centres
An open approach and organisational control over data
Low-code and no-code tools that make AI accessible to every team

Section 5 Lab: Ready-made Gen AI Tools for Work

The Gemini app and Gems for specialised tasks
Gemini Enterprise: searching organisational data, working with documents and prebuilt agents
Gemini for Google Workspace in Gmail, Docs, Sheets and Meet
Lab: create a work assistant Gem and summarise documents with NotebookLM

Section 6 Gen AI for Customer Experience

Agent Search for searching information across the organisation's websites and apps
Customer Engagement Suite: Conversational Agents, Agent Assist and Conversational Insights
Google Cloud Contact Center as a Service and its business value
Case study: improving customer service with gen AI

Section 7 Agent Platform and Agent Tooling

Gemini Enterprise Agent Platform (formerly Vertex AI): Model Garden, Agent Search and AutoML
Prebuilt RAG with Agent Search and RAG APIs
How agents use tools: extensions, functions, data stores and plugins
Prebuilt APIs such as Speech-to-Text, Translation, Document AI and Vision, and when to use Agent Studio or Google AI Studio

Section 8 Lab: Prompt Engineering and Model Limitations

Foundation model limitations: knowledge cutoff, bias, hallucinations and edge cases
Zero-shot, one-shot, few-shot, role prompting and prompt chaining
When to use chain-of-thought and ReAct prompting
Set a clear role, context and answer format for business tasks
Lab: refine prompts for real business tasks and compare the results

Section 9 Lab: Grounding, RAG and Quality Monitoring

Grounding with enterprise data, third-party data and Grounding with Google Search
How RAG lets answers draw on the organisation's latest information
Sampling parameters: temperature, top-p, token count and safety settings
Human in the loop, fine-tuning and tracking model drift and KPIs
Lab: compare answers before and after grounding in Google AI Studio

Section 10 Workshop: Gen AI Strategy, Secure AI and Responsible AI

Steps to bring gen AI into the organisation, choosing a solution and measuring impact
The Secure AI Framework (SAIF), IAM and Security Command Center
Responsible AI: privacy, bias, transparency and accountability
Protect personal data with anonymisation and pseudonymisation
Workshop: plan an organisational use case and review with the sample questions

Schedule & training options

For individuals — public rounds

No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770

Join the waiting list Ask about rounds

For organisations — in-house / private

Tailor the content to your team’s tools and projects
Your dates, at your office or live online
Quotation with tax ID for procurement

Corporate training quote

Instructors

Samit KoyomFounder & CEO, IT Genius Engineering

Frequently asked questions

What is grounding, and how does it make generative AI answers more accurate?

Grounding ties a model's answers to trusted sources, such as web search results or an organisation's documents and databases, rather than relying only on what the model learned in training. A common method is RAG, which retrieves relevant information first and passes it to the model to answer from. The result is answers that are current, consistent with your data, able to cite sources and less prone to hallucination.

What should you consider when choosing generative AI use cases for an organisation?

Start with a clear, measurable business problem, such as cutting customer response times or repetitive paperwork. Then assess data readiness, including quality and accessibility, the level of risk if answers are wrong and how much human review is needed. Next, choose between ready-made tools and building on a platform, define success metrics, and put secure AI and responsible AI measures in place from the outset.

What is an AI agent, and how does it differ from a typical chatbot?

An AI agent uses a language model to reason: it takes a goal, plans the steps and carries them out by calling tools such as search, APIs, document readers or business systems. A typical chatbot answers from the model's own knowledge and stops at the end of the conversation, whereas an agent suits multi-step work, such as answering a customer while checking their order status, provided permissions and human checkpoints are clearly defined.

Who is the Google Cloud Generative AI Leader course for, and what should I know beforehand?

It suits executives, team leads and project managers making decisions on generative AI, business, product, sales and presales teams, digital transformation teams, and people preparing for the Google Cloud Certified Generative AI Leader exam. It is a beginner, no-code course with no programming or machine learning required, though some experience with Gemini or another chatbot helps.

What will I be able to do after the Google Cloud Generative AI Leader course?

You will be able to explain core generative AI and foundation model concepts, assess data readiness, match Google Cloud's gen AI models and services to use cases, explain the role of agents, improve outputs with prompt engineering, grounding and RAG, and plan gen AI adoption with success measures. Participants receive a Certificate of Completion with its own number that can be verified online, separate from the Google Cloud exam.

What do I need for the Google Cloud Generative AI Leader course, and is the exam included?

Bring a laptop with a browser and a Google account to try Gemini, Google AI Studio and NotebookLM; no Google Cloud account is required, and Gemini Enterprise and Gemini for Google Workspace are demonstrated. Bring a work problem from your organisation that you would like to apply AI to for the workshop. The course is not official Google Cloud training, does not include an exam voucher, and you register for the exam yourself.