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Advanced GenAI Development Practices
Course
Advanced GenAI Development Practices
Master the integration of generative AI in software development, leveraging tools like GitHub Copilot and ChatGPT. Learn how to enhance efficiency, improve code quality, design AI-driven APIs, and build full stack applications.
Flexible Schedule
Intermediate Level
Mentor Support
Estimated Time
3-4 hours/week
Using AI to streamline your development workflows and tackle complex challenges is essential for staying competitive. In this course, youll learn how to integrate advanced generative AI tools into your software development practices to enhance efficiency, improve code quality, and achieve better project outcomes. Building on foundational development and AI concepts, youll explore advanced techniques in AI-driven API design, multi-layer integrations, and full-stack application development.
This course emphasizes practical, real-world applications, equipping you with the tools and skills needed to handle complex software projects. Through hands-on experience with platforms such as GitHub Copilot and ChatGPT, youll explore AI-driven techniques to construct multi-step prompts and optimize integration across development layers.
By the end of this course, youll have the experience to seamlessly integrate AI-powered solutions into your development workflows.
This course comprises five purposely designed modules that take you on a carefully defined learning path.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules or submitting assignments. To give you an idea of how long the course takes to complete, it is anticipated that if you work 3-4 hours per week, you will complete the course in 5 weeks. However, as long as the course is completed before the end date, you can work at your own pace.
The materials for each module will become available when you start the particular module. Methods of learning and assessment will include videos, reading material, online exams questions, and a final peer review assignment.
Once you have successfully completed the course, you will earn your Certificate of Completion.
By the end of this course, you will be able to:
- Apply advanced AI prompt engineering techniques.
- Design APIs integrated with AI capabilities.
- Implement full stack AI solutions.
- Automate workflows using AI-driven approaches.
- Practice collaborative development effectively.
- Experienced software developers & engineers
- Full stack developers building AI-powered apps
- Technical leads / architects designing AI-driven systems
- Engineering managers improving workflows with AI
- At least 2 years of software development experience
Course Outline
Video: Welcome to Advanced AI Development Practices (4:00)
Reading: Course Overview
Reading: How to Succeed in This Course
Reading: Grading Scheme
Reading: Helpful Tools and Resources
Module Introduction and Learning Objectives
Video: Introduction to AI-Driven Solution Design (6:39)
Reading: Real-World Applications of AI in Solution Design
Video: Key Frameworks for AI-Assisted Solution Design and Development (8:21)
Reading: Advanced Principles of Generative AI in Software Development
Video: Identifying Complex Problems AI Can Solve (5:16)
Video: AI-Powered Problem Solving: Strategies for Large-Scale Systems (5:20)
Reading: AI Techniques for Addressing Complex Software Challenges
Video: Introduction to Multi-Step Prompt Engineering (5:41)
Reading: Techniques for Designing Effective Multi-Step AI Prompts
Video: Debugging and Refining AI Prompts (6:14)
Lab: Create and test a multi-step prompt for solving a complex software problem
Practice Quiz: Advanced Solution Design
Reading: Module Summary: Advanced Solution Design
Graded Quiz: Advanced Solution Design
Module Introduction and Learning Objectives
Video: Introduction to AI-Driven API Design (6:39)
Reading: Benefits of Using AI for API Development
Video: AI-Assisted API Documentation Automation and Endpoint Generation (6:02)
Video: Overview of AI Applications in Database Operations (4:07)
Video: Automating Database Schema Creation with AI Tools (6:57)
Reading: Techniques for AI-Driven Database Performance Analysis
Video: AI for Database and API Security (6:32)
Reading: Identifying Vulnerabilities Using AI-Driven Tools
Video: AI Solutions for Data Privacy and Compliance (5:23)
Reading: Best Practices for Securing AI-Integrated Systems
Reading: Performance Bottlenecks in Database and API Systems
Video: AI-Assisted API and Database Performance Optimization (6:27)
Lab: Use AI to Optimize the Performance of a Provided API
Practice Quiz: AI for Scalable, Secure, and Optimized Software Systems
Introduction This module explored how generative AI tools are reshaping the landscape of database and API development. By automating routine tasks, enforcing best practices, and providing intelligent insights, AI-driven solutions empower developers to focus on innovation and problem-solving. This summary consolidates the key concepts, practical applications, and best practices covered throughout the lessons, equipping you with actionable strategies for leveraging AI in your professional workflows. Automating API design and documentation Generative AI tools streamline API design by automating repetitive tasks such as endpoint creation, specification writing, and documentation generation. These tools analyze existing code comments, function signatures, and endpoint patterns to propose best-practice structures and generate up-to-date documentation, including industry-standard OpenAPI specifications. Key benefits include: Reduced manual effort and human error in specification and documentation Consistent application of RESTful standards and intelligent naming conventions Accelerated onboarding for new developers, thanks to comprehensive and current API docs Despite these advances, human validation remains critical. Developers must review AI-generated outputs to ensure that APIs meet unique business requirements and comply with organizational standards. Enhancing database operations with AI AI-driven tools significantly improve the efficiency and reliability of database workflows. From schema design to ongoing maintenance, these solutions automate complex and tedious processes, freeing developers to focus on higher-value tasks. Core applications include: Automated generation of migration scripts and compatibility checks during upgrades Proactive monitoring of performance metrics to predict and address bottlenecks Streamlined schema design based on business requirements and data relationships Automated data migration and validation For example, AI can transform business requirements into normalized database schemas, generate SQL DDL statements, and recommend indexing strategies. Human oversight is essential for validating these outputs, particularly to detect issues like circular dependencies, missing constraints, or inefficient design patterns. Proactive performance optimization Performance bottlenecks in databases and APIs often stem from inefficient queries, poor indexing, excessive data transfer, or suboptimal caching. Generative AI tools address these challenges by continuously analyzing system logs, usage patterns, and performance metrics to identify optimization opportunities. AI-powered approaches include: Dynamic tuning of database configurations and API parameters Automated detection of slow queries and resource contention Generation of optimized code solutions and monitoring alerts For instance, if an AI tool detects a spike in API response times or identifies a recurring slow query, it can suggest corrective actions such as query rewriting, index creation, or server scalingoften before end-users are affected. AI for security and compliance Security and regulatory compliance are critical in modern software development. AI-driven tools help enforce data governance by automating privacy impact assessments, data masking, anonymization, and retention policy enforcement. Key capabilities include: Real-time analysis of data flows to generate accurate compliance documentation Automated detection of vulnerabilities, such as SQL injection risks, excessive data exposure, and insecure API endpoints Simulation of attack scenarios to monitor system responses and identify weaknesses Continuous monitoring and robust access controls are essential to safeguard against unauthorized actions and data leaks. AI tools support these efforts by providing actionable insights and automating routine security checks, but human review and incident planning remain necessary to address emerging threats. Best practices for integrating AI into development workflows To maximize the benefits of generative AI in database and API development, consider the following guidelines: Choose tools that align with your team's needs, technology stack, and compliance requirements Provide clear, structured prompts and business requirements to guide AI outputs Employ iterative cycles of generation and human validation to ensure quality and relevance Establish policies for logging, monitoring, and reviewing AI-generated code and documentation Maintain strong access controls and prepare for incidents related to AI tool usage. By embedding these practices into your workflows, you can harness AI's efficiency and intelligence while maintaining high standards for security, reliability, and compliance. Continuing your AI-assisted development journey The field of AI-assisted software development is rapidly evolving. To stay ahead, pursue ongoing learning by: Exploring advanced AI techniques and new toolsets as they emerge Participating in professional communities focused on AI in software engineering Reviewing case studies and industry reports to understand real-world applications and outcomes Regularly updating your knowledge of regulatory changes and best practices in data governance By remaining proactive and adaptive, you can continue to expand your skills and leverage AI for greater impact in your development projects. Summary Generative AI is revolutionizing database and API development by automating complex tasks, enhancing performance, and strengthening security and compliance. While AI tools boost efficiency and accuracy, human oversight remains vital to ensure quality and adherence to standards. By following best practices and committing to continuous learning, developers can effectively integrate AI into their workflows to unlock new capabilities and drive innovation.
Graded Quiz: AI for Scalable, Secure, and Optimized Software Systems
Module Introduction and Learning Objectives
Video: AI in Full-Stack Integration (5:51)
Reading: Practical Applications of AI in Multi-Layer Development
Reading: Common Challenges in Full-Stack Workflows and AI Solutions
Video: How AI Coordinates Multi-Layer Operations (6:09)
Reading: AI-Driven Techniques for Aligning Database, Server, and Client-Side Processes
Reading: Best Practices for Integrating AI in Full-Stack Workflows
Video: AI-Assisted Debugging for Complex Full-Stack Systems (5:44)
Reading: AI Tools for Debugging Full-Stack Software
Reading: Techniques for AI-Driven Testing Across Software Layers
Lab: Apply AI tools to Debug Software
Practice Quiz: AI-Assisted Full-Stack Integration
Reading: Module Summary: AI-Assisted Full-Stack Integration
Graded Quiz: AI-Assisted Full-Stack Integration
Module Introduction and Learning Objectives
Video: Introduction to AI in Team Collaboration (5:55)
Reading: AI-Assisted Tools for Collaboration and Code Reviews
Lab: Use an AI Tool to Review Code in a Team Setting
Reading: Enhancing Workflow Efficiency with AI in Team Environments
Video: AI-Assisted Task Automation and Workload Management (5:28)
Reading: Optimizing Team Workflows with AI Tools
Lab: Use an AI Tool to Analyze and Optimize a Team Workflow
Video: Common Challenges in AI-Driven Collaboration (6:15)
Reading: Strategies for Managing AI Adoption in Teams
Reading: Addressing Communication and Technical Hurdles with AI
Practice Quiz: Building Team AI Workflows
Reading: Module Summary: Building Team AI Workflows
Graded Quiz: Building Team AI Workflows
Module Introduction and Learning Objectives
Final Project: Document and Reflect on AI-Assisted Software Development Workflows
Final Project: Submission and Evaluation
Video: Course Summary (3:19)
Reading: Glossary: Advanced GenAI Development Practices
Final Exam: Advanced GenAI Development Practices
Reading: Congratulations and Next Steps
Reading: Thanks from the Course Team
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FAQs
Youll learn how to build multi-step prompts, design AI-driven APIs, and integrate generative AI tools across front-end, back-end, and database layers. This Advanced GenAI course also helps you apply AI techniques to full stack development scenarios and evaluate different approaches for modern software projects.
The GenAI course for developers focuses on hands-on exercises where you construct prompts, optimize workflows, and use AI to streamline coding tasks. Youll work with tools like GitHub Copilot and ChatGPT to solve real development challenges efficiently.
This course is ideal for engineers who want to apply generative AI for software developers concepts to real-world projects. Developers aiming to automate tasks, improve code quality, and enhance integration workflows will benefit the most.
The course expands on generative AI for software developers by focusing on advanced implementation. Youll analyze integration complexities, apply automation techniques, and build AI-supported full-stack components using practical development tools.
The Advanced GenAI course covers API architecture, layer-to-layer integrations, and advanced prompting strategies. It is designed for learners who understand core development practices and want to use AI to solve more complex engineering scenarios.
Yes, the course applies generative AI for software development concepts such as automated code generation, structured prompt engineering, and AI-assisted debugging. Youll also explore best practices for integrating AI tools into established development workflows.
The generative AI for software developers course helps reduce repetitive work by teaching you how to construct prompts that handle multi-step tasks. Youll use AI tools to generate boilerplate code, refine logic, and validate implementation across development layers.
Yes, the generative AI for software development course is especially useful for full-stack developers who want to enhance API design, manage back end logic, and streamline front-end integration using AI-supported techniques.
Youll work with leading AI tools, including GitHub Copilot, ChatGPT, and other development platforms that support automation and prompt engineering. These tools help apply the principles taught in the advanced generative AI for developers learning path.
Yes, the course equips you with skills to support large-scale development through AI-driven techniques. Youll learn to automate components, optimize architecture, and apply advanced GenAI methods to manage complex project requirements.
Type of certificate
IBM Certificate
About this course
05 Modules
05 Skills
Includes
Discussion space
05 Hands-on labs
04 Practice quizzes
04 Graded quizzes
01 Final project
01 Final exam
Create
Multi-step prompt for solving a complex software problem.
Exercises to explore
AI to Optimize the Performance of a Provided API
AI tools to Debug Software
AI Tool to Review Code in a Team Setting
AI Tool to Analyze and Optimize a Team Workflow
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