AI-Powered SDLC: Requirements to Production
Master the modern Software Development Life Cycle (SDLC), Agile software delivery, and practical applications of Generative AI—from business requirements to production and continuous improvement.
Software development is changing rapidly. Agile, DevOps, cloud-native technologies, and Generative AI are transforming how software teams plan, design, build, test, deploy, and operate applications.
But understanding individual tools is not enough. Successful software delivery requires a complete understanding of the lifecycle, the decisions made at each stage, and how people, processes, architecture, and AI work together.
This course provides a structured, end-to-end understanding of modern software development, combining established engineering practices with practical AI-assisted workflows.
What You’ll Learn
Throughout the course, you will explore:
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Modern Software Development Life Cycle: Understand SDLC models, lifecycle phases, Agile delivery, and the transition toward AI-augmented software engineering.
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Agile, Scrum, and DevOps: Learn how Agile teams organize work, manage backlogs, plan sprints, and deliver software incrementally.
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Product Discovery and Requirements Engineering: Translate business needs into product goals, user stories, acceptance criteria, and traceable requirements.
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AI-Powered Requirements: Explore how Generative AI can assist with requirements analysis, ambiguity detection, story generation, backlog refinement, and documentation.
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Software Architecture and Solution Design: Understand architectural styles, quality attributes, high-level and low-level design, API contracts, data models, security, and architecture decisions.
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AI-Assisted Architecture: Explore AI-supported design alternatives, architecture trade-offs, ADRs, diagrams, API design, and threat modeling.
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Planning and Agile Delivery: Understand estimation, sprint planning, dependency management, capacity planning, and AI-assisted reporting.
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AI-Assisted Development: Explore coding assistants, code generation, code reviews, refactoring, secure coding, and software development best practices.
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Testing and Quality Engineering: Learn unit, integration, system, and acceptance testing, including AI-assisted test generation and defect analysis.
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DevSecOps and CI/CD: Understand automated pipelines, security checks, infrastructure as code, deployment strategies, release management, and rollback.
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Production Operations and Observability: Explore monitoring, logs, metrics, traces, incident management, troubleshooting, and AI-assisted operational analysis.
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Security, Governance, and Responsible AI: Understand secure SDLC practices, AI governance, human review, compliance, and accountability.
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Maintenance and Modernization: Explore technical debt, dependency management, legacy modernization, feedback-driven improvements, and continuous delivery.
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The Future of Software Engineering: Understand how AI-assisted workflows are evolving toward agentic AI and increasingly automated software delivery.
Learn Through the SmartShop Case Study
Instead of treating every SDLC phase as an isolated topic, the course follows SmartShop, a fictional eCommerce application, throughout its software delivery journey.
You’ll see how a business requirement progresses through product discovery, requirements engineering, architecture, Agile planning, development, testing, deployment, production monitoring, incident management, and continuous improvement.
The case study also introduces an AI-powered shopping assistant to illustrate the architectural and operational considerations involved in delivering AI-enabled software features.
This connected learning approach helps you understand how the different SDLC activities fit together in real software projects.
AI-Powered Software Engineering with Human Accountability
Generative AI can accelerate many software engineering activities, but its output must be evaluated carefully.
Throughout this course, we follow one important principle:
AI proposes. Humans review. Humans decide.
You’ll learn where AI can improve productivity, where its limitations matter, and why human judgment remains essential for architecture, security, quality, and production decisions.
Who Should Take This Course?
This course is suitable for:
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Software developers and engineers
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Solution architects and software architects
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Business analysts and product owners
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Scrum Masters and Agile practitioners
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QA and software testing professionals
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DevOps and platform engineers
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Engineering managers and technical leads
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IT students and aspiring software professionals
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Technology professionals exploring Generative AI in software delivery
Why Enroll?
This course connects the complete software development lifecycle with modern Agile, DevSecOps, and AI-assisted engineering practices.
With 108 structured video lectures across 14 sections, you’ll build a broader understanding of how software moves from an initial idea into a production system—and how AI can support the teams responsible for delivering it.
Whether you want to strengthen your software engineering foundation, understand end-to-end delivery, or explore responsible AI adoption across the SDLC, this course provides a practical learning roadmap.
Join me and discover how modern software is designed, developed, delivered, and continuously improved in the AI era.
Mohammad Naushad
Architect With Purpose
