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⇱ GenAI for Application Developers | Coursera

GenAI for Application Developers

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Gain insight into a topic and learn the fundamentals.
4.9

31 reviews

Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.9

31 reviews

Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Identify the capabilities of Gemini for Google Cloud (Duet AI) for application development functions.

  • Examine real-world applications to leverage GenAI for streamlining work and fostering innovation in application development.

  • Deploy strategies & tactics to responsibly integrate GenAI into application development practices while maintaining human oversight & accountability.

Details to know

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Assessments

2 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Building Smarter Software: GenAI Tools and Practices Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There is 1 module in this course

“GenAI for Application Developer” is tailored for professionals eager to integrate AI into their development workflow. This comprehensive course introduces Gemini for Google Cloud (Duet AI), emphasizing its potential to streamline coding, debugging, and deployment processes. Learners will gain hands-on experience with Gemini for Google Cloud (Duet AI) tools, learning how to leverage them for enhanced productivity and efficiency in application development.

This course is designed for team leads, managers, senior developers, and software engineers. It is ideal for those who wish to integrate GenAI into their strategic initiatives for enhanced productivity, streamline their workflows, and advance their careers by mastering cutting-edge GenAI applications in application development. Participants should have a basic understanding of software development, debugging, and deployment processes. Familiarity with programming languages like Python, Java, or JavaScript is recommended. An open mindset towards incorporating Generative AI (GenAI) tools and techniques, along with a curiosity to experiment and learn, will help maximize the benefits of this learning experience. By the end of the course, learners will have a robust understanding of GenAI for application development. They will be able to implement Gemini for Google Cloud (Duet AI) in their projects to accelerate development cycles, reduce errors, and maintain high standards of code quality.

The course covers the essentials of GenAI for application development, from Gemini for Google Cloud (Duet AI)’s foundational models and integration with Google Cloud services to practical applications in real-world development scenarios. Learners will explore various features of GenAI for application development, such as code generation, error correction and deployment automation.

What's included

6 videos5 readings2 assignments1 peer review

6 videos•Total 53 minutes
  • Introduction to GenAI for App Developers•7 minutes
  • History & Background for GenAI and App Developers•10 minutes
  • Demo for Code Generation, Completion and Explanation with Gemini for Google Cloud•13 minutes
  • Ethical Concerns and Remediating Risks•7 minutes
  • Demo for Best Practices in Gemini for Google Cloud•13 minutes
  • Closing Thoughts: What’s Next•3 minutes
5 readings•Total 45 minutes
  • Our Roadmap & Resources Available: How to Get Started•5 minutes
  • GenAI and App Development Glossary•10 minutes
  • Demo for Integrating Google Cloud Environment with Gemini•10 minutes
  • Demo for Managing and Integrating API Functionalities with Python and Flask•10 minutes
  • Demo for Integrating Inventory on Cloud Run with Gemini•10 minutes
2 assignments•Total 50 minutes
  • GenAI for Application Developer•20 minutes
  • GenAI Web Application Development•30 minutes
1 peer review•Total 15 minutes
  • [optional] Practice Project for App Developers•15 minutes

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Instructors

Instructor ratings
4.8 (7 ratings)

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Frequently asked questions

In this course, GenAI-assisted application development means using generative AI as part of everyday software work instead of treating it as a separate add-on. The emphasis is on using it to support coding, debugging, testing, explanation, and deployment-related work while keeping human oversight and accountability.

You would use it when you need help moving through common development tasks such as writing code, understanding unfamiliar code, fixing errors, or creating tests. The course treats it as especially useful when work would otherwise involve repeated context switching between documentation, examples, and manual troubleshooting.

It fits into the middle of the development workflow, where ideas are turned into working code, reviewed, and refined. In this course, it acts as a support layer across build, test, and deployment preparation rather than a one-time step at the end.

Traditional manual development relies more heavily on separate searches, documentation checks, and hand-written trial and error for each problem. Here, GenAI-assisted development is presented as a more conversational and iterative way to generate, explain, and improve code while the developer still reviews the output.

A basic understanding of software development, debugging, and deployment is helpful, and some familiarity with a language like Python, Java, or JavaScript is recommended. What matters most is being able to follow common development tasks and being open to experimenting with generative AI tools.

The course centers on Gemini for Google Cloud as the main generative AI tool for developer workflows. It also emphasizes prompt-writing best practices and responsible use with human oversight.

You practice writing better prompts, generating and refining code, asking for code explanations, and using AI to support testing and debugging. You also apply it to integration and deployment-related tasks so the tool becomes part of a repeatable development workflow.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.