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

Ce cours n'est pas disponible en Français (France)

Nous sommes actuellement en train de le traduire dans plus de langues.

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

Instructeurs : Starweaver

Inclus avec En savoir plus

Demander à Coursera

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

8 heures à compléter

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

8 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Software Testing
Catégorie : Cloud API
Catégorie : Prompt Patterns
Catégorie : Prompt Engineering Tools
Catégorie : Engineering Software
Catégorie : Test Script Development
Catégorie : Continuous Deployment
Catégorie : Test Tools
Catégorie : DevOps
Catégorie : Application Design
Catégorie : AI Integrations
Catégorie : Code Review
Catégorie : Test Automation
Catégorie : Software Documentation
Catégorie : CI/CD
Catégorie : Responsible AI
Catégorie : Development Testing

Outils que vous découvrirez

Catégorie : Generative AI
Catégorie : GitHub Copilot
Catégorie : Prompt Engineering

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

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Évaluations

4 affectations¹

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Il y a 4 modules dans ce cours

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.

Inclus

11 vidéos3 lectures1 devoir1 évaluation par les pairs1 sujet de discussion

11 vidéosTotal 58 minutes

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

3 lecturesTotal 15 minutes

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

1 devoirTotal 20 minutes

AI-Assisted Coding Fundamentals20 minutes

1 évaluation par les pairsTotal 10 minutes

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

1 sujet de discussionTotal 5 minutes

Ethical Implications & Integration Challenges5 minutes

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

Inclus

10 vidéos1 lecture1 devoir1 évaluation par les pairs1 sujet de discussion

10 vidéosTotal 66 minutes

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

1 lectureTotal 5 minutes

AI Tools for Developers 5 minutes

1 devoirTotal 20 minutes

Development Workflow Optimization with AI20 minutes

1 évaluation par les pairsTotal 10 minutes

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

1 sujet de discussionTotal 5 minutes

Managing Complexity & Documentation Challenges5 minutes

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.

Inclus

10 vidéos1 lecture1 devoir1 évaluation par les pairs1 sujet de discussion

10 vidéosTotal 57 minutes

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

1 lectureTotal 5 minutes

AI and Software Testing 5 minutes

1 devoirTotal 20 minutes

Testing and Quality Assurance20 minutes

1 évaluation par les pairsTotal 10 minutes

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

1 sujet de discussionTotal 10 minutes

Evolution of the QA Role & Limitations of AI10 minutes

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

Inclus

11 vidéos1 lecture1 devoir2 évaluations par les pairs1 sujet de discussion

11 vidéosTotal 63 minutes

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

1 lectureTotal 5 minutes

AI in DevOps 5 minutes

1 devoirTotal 20 minutes

DevOps and Deployment with Generative AI20 minutes

2 évaluations par les pairsTotal 70 minutes

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

1 sujet de discussionTotal 10 minutes

The Future of Infrastructure with AI10 minutes

Instructeurs

Starweaver

STARWEAVER

598 Cours1 225 217 apprenants

Offert par

STARWEAVER

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Foire Aux Questions

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.

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¹ Certains travaux de ce cours sont notés par l'IA. Pour ces travaux, vos Données internes seront utilisées conformément à Notification de confidentialité de Coursera.