Gen AI Chatbot with Gemini & Chainlit - Develop to Deploy

Want to build your first real Gen AI Chatbot application with Google's Gemini models?

This short, hands-on course takes you from your first Gemini API call all the way to developing & deploying a working AI chatbot on Google Cloud.

You will learn how to use Google's Gen AI SDK to interact with Gemini models, control model behavior, manage tokens and costs, connect models to tools, and build conversational applications.

Then we will put those concepts into practice by building a real chatbot using Gemini, the Gen AI SDK, and Chainlit.

But we won't stop at local development.

You will also learn how to:

• Build a chatbot interface using Chainlit

• Integrate Gemini using the Google Gen AI SDK

• Add custom functionality using tools

• Dockerize your chatbot application

• Build and store your container image using Google Artifact Registry

• Deploy the chatbot to Google Cloud Run

• Test your deployed AI application in the cloud

The course is intentionally short and focused on practical development. Rather than spending hours on theory, you will learn the essential concepts and immediately apply them by building a working application.

What You Will Learn

By the end of this course, you will be able to:

• Understand the fundamentals of Google's Gen AI SDK

• Work with Google's Gemini model family

• Create applications using the Gemini API

• Configure system instructions, temperature, and safety settings

• Understand and manage tokens and Gen AI costs

• Ground a model using tools

• Create and use custom tools with Gemini

• Build a conversational chatbot using Chainlit

• Package your chatbot using Docker

• Push container images to Google Artifact Registry

• Deploy your Gen AI chatbot to Google Cloud Run

• Take a Gen AI application from local development to a cloud deployment

Course Content:

Section 1: Getting Started with Google's Gen AI SDK

Start by understanding the Google Gen AI SDK and how it can be used to build applications with Gemini.

You will create a Gemini API key, explore Google's model family, and write your first Gemini application.

You will then learn how to control model behavior using system instructions, temperature, and safety settings.

We will also look at token usage and cost management before exploring how models can be grounded using tools and custom functions.

Finally, you will learn how to use the chat functionality provided by the Gen AI SDK.

Section 2: Build and Deploy a Chatbot

Now we put everything together.

You will build a complete chatbot using:

Gemini + Google Gen AI SDK + Chainlit

You will start with a basic chatbot and gradually turn it into a more complete application.

You will then:

Develop → Test → Dockerize → Push to Artifact Registry → Deploy to Cloud Run

By the end of this section, you will have a working Gen AI chatbot running on Google Cloud.

Why Take This Course?

Many Gen AI courses stop after showing you how to send a prompt to an LLM.

This course takes the next step.

You will learn how to take a Gen AI model and turn it into an actual application with a user interface, package it as a container, and deploy it to Google Cloud.

The goal is simple:

Build something that actually works.