Your Gen AI Quickstart with Gemini & Vertex AI

Embarking on the journey of building generative AI applications is now more accessible with tools like Google’s Gemini and Vertex AI. These platforms offer robust features to streamline the development and deployment of AI-driven solutions. Here’s a concise guide to help you get started:

1. Setting Up Your Environment

• Google Cloud Project: Begin by creating a new project in the Google Cloud Console. Ensure that billing is enabled for your project, as it’s required for utilizing Vertex AI services.
• Enable Vertex AI API: Within your project, navigate to the Vertex AI section and enable the necessary APIs. This activation is crucial for accessing Vertex AI’s full capabilities.
• Google Cloud CLI: Install the Google Cloud Command Line Interface (CLI) to manage your project from the terminal. After installation, initialize it using gcloud init and authenticate with your Google account.

2. Exploring Gemini Code Assist

Google’s Gemini Code Assist is a powerful tool designed to enhance your coding efficiency:

• Free Access for Individuals: As of February 25, 2025, Gemini Code Assist is available for free to individual users, including students, hobbyists, freelancers, and startups. This offering provides up to 180,000 code completions per month, significantly surpassing other free coding assistants. The Verge
• Integration with IDEs: Gemini Code Assist seamlessly integrates with popular development environments like Visual Studio Code, GitHub, and JetBrains, supporting all programming languages.
• Features: Utilize natural language commands to generate code snippets, debug, and modify existing applications, thereby accelerating your development process.

3. Building AI Applications with Vertex AI and Gemini

To practically apply these tools, consider engaging with hands-on tutorials and labs:

• AI Recipe Haven Project: This codelab guides you through creating a Python and Streamlit-based web application named “AI Recipe Haven.” You’ll learn to:
  • Activate and use Gemini Cloud Assist.Develop a chatbot using Vertex AI Agent Builder.Deploy the application on Cloud Run.Integrate AI search and chat features grounded in Cloud Storage and BigQuery.
  This comprehensive lab offers a step-by-step approach to building and deploying AI applications.

codelabs.developers.google.com

https://cloud.google.com/vertex-ai/pricing
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• Grounding Techniques for Gemini: Understanding grounding techniques is essential for enhancing the accuracy of AI model responses. A 36-minute video titled “Grounding for Gemini with Vertex AI Search and DIY RAG” delves into:
  • Implementing grounding techniques for generative AI.Building a production-ready Retrieval-Augmented Generation (RAG) system.Integrating Gemini with Vertex AI Search and custom datasets.

4. Deploying AI Applications

Once your application is developed, deploying it efficiently is the next step:

• Using Cloud Run: Deploy your Streamlit applications on Google Cloud’s serverless platform, Cloud Run. This service allows you to run your applications in a scalable and cost-effective manner.
• Docker Integration: Create a Dockerfile to containerize your application, facilitating seamless deployment on Cloud Run.

For a practical example, refer to the tutorial “Build and Deploy a Gen AI App Using Vertex AI, Gemini, and Streamlit,” which provides detailed instructions on setting up your environment, developing the application, and deploying it using Cloud Run.

By leveraging Gemini and Vertex AI, you can accelerate the development of generative AI applications, from coding assistance to deployment. Engaging with these resources will equip you with the knowledge and tools necessary to build innovative AI solutions.