Google AI Studio has moved well past its early days as a simple prompt playground. It now supports natural-language app scaffolding, native vibe coding, Imagen and Veo access, and one-click Cloud Run deployment, all inside a browser tab with no SDK setup required. In this course by kodestree, you will walk through that entire workflow, from your first Gemini prompt to a deployed multimodal application, using the same tools currently shaping how product teams prototype AI features.
Prerequisites
There are no strict prerequisites to enroll – Google AI Studio is built to be approachable for both technical and non-technical learners. That said, the following background will help you move faster:
- A basic understanding of how APIs work is helpful but not mandatory
- Familiarity with any programming language, with Python or JavaScript preferred for the advanced modules
- A Google account, needed to access AI Studio at no cost
- General curiosity about generative AI tools – no prior machine learning experience is required
Course Objectives
- Navigate the Google AI Studio interface and understand where it fits within the broader Gemini ecosystem
- Write and refine prompts using system instructions, temperature controls, and structured output settings
- Build multimodal applications that combine text, image, audio, and video inputs
- Use natural-language app building to scaffold a working front end and back end from a plain description
- Generate and manage a Gemini API key and connect it to an external application
- Deploy a working AI Studio project to Cloud Run with minimal configuration
- Apply prompt design and grounding techniques that reduce hallucination in real-world use cases
- Compare AI Studio against Vertex AI to make informed platform decisions at work
What You Will Learn
- Setting up and navigating the Google AI Studio workspace
- Working with the Gemini model family, including Flash and Pro-tier models, and choosing the right one for a task
- Prompt engineering fundamentals: system instructions, few-shot examples, temperature, top-k and top-p tuning
- Structured output generation using JSON schemas for reliable, machine-readable responses
- Multimodal prompting with image, audio, and video inputs
- Building and testing conversational, multi-turn chat experiences
- Using Imagen for image generation and Veo for short video generation inside AI Studio
- Vibe coding: describing an app in plain language and having Gemini scaffold the codebase
- Live API features, including real-time screen sharing, live audio, and long-context conversations
- Generating a free Gemini API key and integrating it into an external application
- One-click deployment of AI Studio projects to Cloud Run
- Applying grounding and safety settings to reduce hallucinated or unsafe outputs
- Comparing AI Studio and Vertex AI workflows for prototyping versus enterprise-scale deployment
Who Should Take This Course?
This course is built for anyone who wants to move from casually experimenting with Gemini to confidently building and shipping AI-powered tools. It is a strong fit for:
- Software developers and full-stack engineers exploring generative AI integration
- Product managers and business analysts who want to prototype AI features without waiting on engineering bandwidth
- Data scientists and ML practitioners adding prompt engineering to their toolkit
- Students and career changers building a portfolio in applied generative AI
- Marketing, content, and operations professionals looking to automate workflows with Gemini
- IT consultants and solution architects evaluating Google’s AI stack for client projects
Skills You Will Gain
Technical Skills
- Prompt engineering and prompt evaluation
- Multimodal application design across text, image, audio, and video
- API integration using Gemini API keys
- Structured output design with JSON schemas
- Basic cloud deployment using Cloud Run
- Rapid AI prototyping without depending on a dedicated engineering team
- Evaluating model behavior for accuracy and safety before production use
- Communicating AI capabilities and limitations to non-technical stakeholders
- Choosing between AI Studio and Vertex AI based on project scale and governance needs
Tools Covered
- Google AI Studio (aistudio.google.com)
- Gemini 3.5 Pro and Gemini 3.5 Flash models
- Gemini API and API key management
- Imagen for image generation
- Veo for video generation
- Google Cloud Run for deployment
- Vertex AI, covered for comparison and enterprise context
Career Outcomes
Completing this training positions you for roles where organizations are actively hiring for applied Gemini and generative AI skills, including:
- AI / Prompt Engineer
- Generative AI Developer
- AI Product Manager
- AI Solutions Consultant
- Applied AI Analyst
- Full-Stack Developer, AI-integrated applications
- AI Implementation Specialist
| Job Role | Experience Level | India | USA |
|---|---|---|---|
| AI Developer | Entry Level (0–2 years) |
₹5–10 LPA |
$117K–$140K/year |
| Generative AI Developer | Entry to Mid-Level (1–3 years) |
₹8–15 LPA |
$130K–$170K/year |
| AI Application Developer | Mid-Level (3–5 years) |
₹9.5–20 LPA |
$140K–$180K/year |
| AI/ML Engineer | Mid-Level (3–5 years) |
₹12–25 LPA |
$150K–$200K/year |
| Senior AI/ML Engineer | Senior (5–10 years) |
₹20–40+ LPA |
$180K–$220K+/year |
| AI Solutions / GenAI Specialist | Senior (5+ years) |
₹25–45+ LPA |
$190K–$220K+/year |
Why Choose kodestree for this Program?
kodestree’s Google AI Studio training is designed around how the platform is actually used in 2026, not how it looked when it first launched. Here’s what sets the program apart:
- Live, instructor-led sessions with hands-on lab access
- Curriculum updated to reflect Gemini 3.5 and current AI Studio features
- Real deployment practice using Cloud Run, not just prompt demos
- Flexible weekday and weekend batches
- Lifetime access to session recordings and course material
- Dedicated doubt-clearing sessions and community support
- Course completion certificate recognized by hiring partners
- 24/7 learner support
