Do I need AI experience to take this course?

No prior AI experience is required. You need basic programming knowledge and familiarity with software development. This AI course starts by setting up AI coding assistants and teaching prompt patterns, so the AI side is built from the ground up.

What will I build in this course?

Practical deliverables across four modules: integrating an AI assistant into a working project, managing documentation complexity, evaluating AI limitations in QA, hardening a CI/CD pipeline against automation risk, and a sixty-minute capstone integrating AI tools for app development across the full software development lifecycle.

Does this course include a certificate?

Yes. Completing it earns a shareable certificate you can add to your LinkedIn profile.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

AI for Application Development: End-to-End AI Workflow

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kurs ist nicht verfügbar in Deutsch (Deutschland)

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AI for Application Development: End-to-End AI Workflow

Dozenten: Starweaver

Bei Mehr erfahren enthalten

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

8 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

8 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

Explain where generative AI fits across the software development lifecycle, from requirements to deployment.
Apply AI tools for app development such as GitHub Copilot to accelerate coding, testing, and documentation.
Use AI-driven analysis to improve API design, database structure, performance, and application security.
Develop and deploy complete solutions through effective AI for application development integration.

Kompetenzen, die Sie erwerben

Kategorie: Software Technical Review
Kategorie: Development Testing
Kategorie: Maintainability
Kategorie: Application Deployment
Kategorie: Engineering Software
Kategorie: Software Development
Kategorie: Cloud API
Kategorie: Code Review
Kategorie: Software Design Documents
Kategorie: Test Automation
Kategorie: DevOps
Kategorie: Test Tools
Kategorie: CI/CD
Kategorie: Continuous Deployment
Kategorie: Verification And Validation
Kategorie: Software Testing
Kategorie: Software Documentation

Werkzeuge, die Sie lernen werden

Kategorie: Generative AI
Kategorie: AI Workflows
Kategorie: Prompt Engineering

Wichtige Details

Zertifikat zur Vorlage

Zu Ihrem LinkedIn-Profil hinzufügen

Kürzlich aktualisiert!

August 2026

Bewertungen

4 Zuweisungen¹

KI-bewertet siehe Haftungsausschluss

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

Weitere Informationen zu Coursera für Unternehmen

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In diesem Kurs gibt es 4 Module

Generative AI is transforming how software is designed, built, tested, and deployed, becoming an essential tool for developers, engineers, architects, and technology leaders. Rather than replacing human expertise, AI for application development enhances productivity by accelerating workflows, improving code quality, and automating repetitive tasks. This course provides a practical, end-to-end journey through the software development lifecycle, showing how AI can be integrated into coding, documentation, testing, DevOps, and deployment.

Learners begin with AI-assisted coding using AI tools for app development such as GitHub Copilot, along with prompt engineering and code generation, before exploring AI-powered requirements analysis, API development, database design, and documentation. The course then covers AI-driven testing, bug detection, performance optimization, and security, followed by DevOps automation, Infrastructure as Code, CI/CD, monitoring, and incident response. Through hands-on labs, quizzes, discussions, and a capstone project, learners gain practical experience building AI apps and applying AI across real development workflows. Designed for developers, QA engineers, DevOps professionals, and architects with basic programming knowledge, this four-hour AI for application development course equips learners to confidently integrate generative AI into software engineering while balancing innovation with responsible human oversight.

This inaugural module provides the foundational knowledge and practical skills for integrating Generative AI into the core process of writing and managing code.

Das ist alles enthalten

11 Videos3 Lektüren1 Aufgabe1 peer review1 Diskussionsthema

11 VideosInsgesamt 58 Minuten

Intro Video to Course 3 Minuten
Module Introduction 1 Minute
Overview of Code Generation Tools 5 Minuten
Setting Up AI Coding Assistants 8 Minuten
Prompt Patterns for Coding 7 Minuten
Generating Simple Functions and Classes 6 Minuten
Boilerplate and Complex Structures 7 Minuten
Case Study: AI Code Generation Project 5 Minuten
Code Review & Security Analysis 6 Minuten
Refactoring for Maintainability 4 Minuten
Testing and Debugging AI-Generated Code 5 Minuten

3 LektürenInsgesamt 15 Minuten

Welcome to the Course: Course Overview5 Minuten
AI in Modern Software Development 5 Minuten
Choosing the Right AI Tool for Your Project 5 Minuten

1 AufgabeInsgesamt 20 Minuten

AI-Assisted Coding Fundamentals20 Minuten

1 peer reviewInsgesamt 10 Minuten

Hands-On-Learning: Integrating an AI Assistant into a Simple Project 10 Minuten

1 DiskussionsthemaInsgesamt 5 Minuten

Ethical Implications & Integration Challenges5 Minuten

This module elevates the application of Generative AI from individual coding tasks to the broader, end-to-end software development workflow.

Das ist alles enthalten

10 Videos1 Lektüre1 Aufgabe1 peer review1 Diskussionsthema

10 VideosInsgesamt 66 Minuten

Module Introduction 1 Minute
Requirements to Code Outlines 7 Minuten
AI for Database Design and Queries 7 Minuten
Case Study on AI in Design Phase 5 Minuten
Generating API Endpoints 5 Minuten
AI for Integration Development 10 Minuten
Debugging APIs with AI 10 Minuten
Generating Inline Code Comments 5 Minuten
Creating Comprehensive Documentation 9 Minuten
Automating Documentation Workflows with AI 7 Minuten

1 LektüreInsgesamt 5 Minuten

AI Tools for Developers 5 Minuten

1 AufgabeInsgesamt 20 Minuten

Development Workflow Optimization with AI20 Minuten

1 peer reviewInsgesamt 10 Minuten

Hands-On-Learning: Managing Complexity & Documentation Challenges 10 Minuten

1 DiskussionsthemaInsgesamt 5 Minuten

Managing Complexity & Documentation Challenges5 Minuten

This module is designed to address the needs of the QA engineer persona by demonstrating how Generative AI can significantly enhance the efficiency and efficacy of the testing process.

Das ist alles enthalten

10 Videos1 Lektüre1 Aufgabe1 peer review1 Diskussionsthema

10 VideosInsgesamt 57 Minuten

Module Introduction 1 Minute
Introduction to AI in Software Testing 4 Minuten
Generating Unit, Integration, and End-to-End Tests 5 Minuten
Case Study on Maximising Test Coverage 5 Minuten
Writing Automated Testing Scripts with AI 7 Minuten
Using AI for Bug Detection and Root Cause Analysis 6 Minuten
AI for Suggesting Bug Fixes 4 Minuten
AI for Performance Optimisation 8 Minuten
Identifying Vulnerabilities with AI 12 Minuten
How AI Anticipates Bugs and Failures Before They Happens 5 Minuten

1 LektüreInsgesamt 5 Minuten

AI and Software Testing 5 Minuten

1 AufgabeInsgesamt 20 Minuten

Testing and Quality Assurance20 Minuten

1 peer reviewInsgesamt 10 Minuten

Hands-On-Learning: Evolution of the QA Role & Limitations of AI 10 Minuten

1 DiskussionsthemaInsgesamt 10 Minuten

Evolution of the QA Role & Limitations of AI10 Minuten

This final, capstone module integrates Generative AI into the deployment and operational pipeline, demonstrating its potential for full-stack automation.

Das ist alles enthalten

11 Videos1 Lektüre1 Aufgabe2 peer reviews1 Diskussionsthema

11 VideosInsgesamt 63 Minuten

Module Introduction 1 Minute
Introduction to AI for Infrastructure as Code 5 Minuten
Generating Terraform and Ansible Code with AI9 Minuten
Case Study on Automated Infrastructure Provisioning 6 Minuten
Optimising CI/CD Scripts with GenAI 7 Minuten
Analysing Build Failures with AI 6 Minuten
Automated Monitoring & Alerting with AI 6 Minuten
Automating Incident Response 5 Minuten
Real-time Performance Analysis and Scaling Suggestions 7 Minuten
Autonomous Reliability Engineering with AI 8 Minuten
Course Wrap-up Video 2 Minuten

1 LektüreInsgesamt 5 Minuten

AI in DevOps 5 Minuten

1 AufgabeInsgesamt 20 Minuten

DevOps and Deployment with Generative AI20 Minuten

2 peer reviewsInsgesamt 70 Minuten

Hands-On-Learning: Preventing "Pipeline Hell" & Automation Risks 10 Minuten
Project: Integrating AI Tools Across the Software Development Life Cycle 60 Minuten

1 DiskussionsthemaInsgesamt 10 Minuten

The Future of Infrastructure with AI10 Minuten

Dozenten

Starweaver

STARWEAVER

612 Kurse1.262.407 Lernende

von

STARWEAVER

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Häufig gestellte Fragen

Yes, and it now spans the entire development lifecycle rather than code completion alone. AI for application development covers code generation, requirements analysis, API and database design, documentation, test generation, bug detection, infrastructure provisioning, and incident response. This AI for application development course works through each of those stages across four modules.

The main categories of AI tools for app development are coding assistants such as GitHub Copilot, generative models used through structured prompting, AI-assisted testing tools, and AI applied to infrastructure and CI/CD. This course teaches GitHub Copilot, generative AI, and prompt engineering directly, then applies them to Terraform, Ansible, and CI/CD pipeline scripts in the final module.

There is no single best tool, and the honest answer is that it depends on the stage. Coding assistants handle boilerplate, functions, and refactoring well. General-purpose models are better for requirements analysis, documentation, and explaining unfamiliar code. Infrastructure work needs a model comfortable with Terraform and Ansible syntax. The transferable skill in AI application development is knowing which tool suits which task and how to prompt it, which is why this course opens with a reading on choosing the right AI tool for a project.

No-code app builders can generate simple applications, but they hit limits quickly on custom logic, integrations, security, and anything that needs maintenance. This course takes a different approach: it teaches developers to build AI apps inside a professional workflow, so you keep architectural control while AI accelerates repetitive work. If you want a tool that builds an app for you with no programming knowledge, this is not that course.

The evidence so far points to augmentation rather than replacement. AI tools for app development generate code quickly but cannot judge whether the architecture is right, whether a test suite is meaningful, or whether a suggested fix addresses the root cause. This course treats that judgment as the point, covering code review, security analysis, and the limitations of AI in QA alongside the productivity gains.

Developers, QA engineers, DevOps professionals, and architects with basic programming knowledge. Each module targets a specific role, with Module 3 built around the QA engineer's workflow and Module 4 around deployment and operations, so this AI app development course suits both individual contributors and technical leads evaluating AI adoption.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.

Weitere Fragen

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Finanzielle Unterstützung verfügbar,

¹ Einige Aufgaben in diesem Kurs werden mit AI bewertet. Für diese Aufgaben werden Ihre Daten in Übereinstimmung mit Datenschutzhinweis von Courseraverwendet.