Gemini & Google AI
A deep, hands-on mastery path for Gemini: models and the app, Gems, Workspace, Extensions, AI Studio and the Gemini API, Gemini CLI and Code Assist, agents, Vertex AI, automation, and a capstone.
Everyone talks about Google AI. You are going to run it. This block takes you from the Gemini model family all the way to a production workflow you actually ship, and it does not waste your time on theory you will never use.
Start with fundamentals: when pro earns its keep and when flash wins, how gems and personalization make Gemini yours, and why multimodality and long context are the superpowers that change how you work. Then you put it to work inside Google Workspace, in docs, gmail, sheets, slides, meet, drive, and the side panel, with the governance and data controls a serious leader has to answer for.
From there you stop being a passenger. You build reusable gems, wire up extensions, and learn exactly what to reach for and when. You prototype in Google AI Studio, make your first real calls to the Gemini API, and ground answers in Google Search so your outputs stand up to scrutiny.
Engineers on your team will respect this next part. You take the Gemini CLI into your terminal, run agentic coding, review pull requests, and go end to end from issue to merged pull request. You stand up agents with the agent development kit, orchestrate several of them, and set the guardrails on permissions, review, and cost that keep autonomy from becoming a liability.
Then you go deeper: function calling, structured outputs, embeddings, RAG on your own files, and Vertex AI to take Gemini to production. The capstone ties it together in one real workflow.
By the end you will not be asking what Gemini can do. You will be deciding what it does next inside your organization.
What you'll master
- Choose the right Gemini model for cost, speed, and quality on any given task
- Deploy Gemini across Workspace while enforcing governance and data controls
- Build reusable gems and connect extensions to your live business apps
- Call the Gemini API and ground responses with Google Search for trustworthy output
- Run agentic coding from the CLI, from issue to merged pull request
- Orchestrate multiple agents with the ADK behind permission, review, and cost guardrails
- Ship a production Gemini workflow using RAG, embeddings, and Vertex AI
Modules
Covers the Gemini model family, when to use each version, and how multimodality, long context, and personalization extend what it can do.
Shows how to use Gemini across Docs, Gmail, Sheets, Slides, Meet, and Drive, plus the governance controls that keep company data protected.
Shows how to build reusable custom assistants, connect Gemini to your apps, and choose the right tool for each task.
Walks through prototyping prompts, making your first API calls, and grounding responses with Google Search and live data.
Shows how to use Gemini for agentic coding across the terminal, IDE, and GitHub, from a single issue to a merged pull request.
Shows how to build, orchestrate, and safeguard AI agents that automate work across Google Workspace using the agent development kit.
Shows how to extend Gemini with function calling, structured outputs, embeddings, and retrieval, then deploy it to production on Vertex AI.
Shows how to use Gemini across Google tools, automate repetitive tasks, and build a complete end-to-end workflow.
Frequently asked questions
What does the Gemini & Google AI block actually cover?
It runs 8 modules and 29 lessons, from the Gemini model family and the Gemini app through Workspace, gems, extensions, Google AI Studio, the Gemini API, the Gemini CLI, agents, RAG and Vertex AI, ending with a capstone workflow. It is part of the AI Essentials track. The emphasis is on shipping something that works rather than on theory about how language models are built.
Do I need to code to get through this?
The first three modules (fundamentals, Workspace, gems and extensions) require no code. From Google AI Studio and the Gemini API onward, and especially in the Gemini CLI and agent modules, you work with API calls, terminal commands and pull requests. Non-technical leaders often read those modules to know what to ask of their engineers rather than to write the code themselves.
Is there a certificate at the end of the capstone?
No. The Gemini & Google AI block delivers no diploma, no state-recognised certification and carries no school or university affiliation. What you leave with is the capstone itself: one real end-to-end Gemini workflow you built. Reading is free and open; an account only saves your progress.
When should I use Gemini pro rather than flash?
That trade-off is the subject of the opening lesson on the Gemini model family. The short version: pro earns its cost on tasks where reasoning quality decides the outcome, flash wins when volume, latency and price dominate. The block treats model choice as a cost-speed-quality decision you make per task, not once for the whole organisation.
What is the difference between a gem and an extension?
A gem is a reusable custom assistant you configure once with your instructions and context; an extension connects Gemini to an external app so it can act on live data. The module on gems and extensions has a dedicated lesson on knowing which one to reach for, because the two solve different problems and are often confused.
How does the block handle the risk of letting agents run autonomously?
The agents module includes a lesson specifically on guardrails: permissions, human review and cost control. Multi-agent orchestration with the ADK is taught alongside those limits rather than after them, and the Gemini CLI module covers loops and autonomous runs with the same caution. The stated principle is that autonomy without guardrails becomes a liability.