Generative AI Foundations for Software Development Life Cycle (SDLC)

Bring generative AI into the software development lifecycle

Generative AI is changing how software teams approach the earliest stages of creating digital products.

On this introductory course, you’ll explore how large language models work and how they can support the Software Development Life Cycle (SDLC) — the process of planning, designing, creating, testing, deploying, and maintaining software.

You’ll examine how generative AI and code-specialised models are trained, what they can do well, and where their limitations lie.

You’ll also explore how AI developer tools fit into modern engineering workflows and where human judgement remains essential.

Shape better technical prompts

Explore prompting patterns designed for software development tasks, including context setting, constraints, and iterative refinement.

You’ll practise adjusting prompts to produce clearer, more relevant outputs and see how different approaches can support technical decision-making.

Support planning, requirements, and design with AI

Apply generative AI to early SDLC activities such as drafting user stories, clarifying specifications, exploring design ideas, and producing technical documentation.

You’ll examine where AI can accelerate routine work while keeping developers responsible for reviewing and validating its output.

Navigate the risks of AI-assisted development

Investigate hallucinations, output quality, security, and intellectual property considerations when using generative AI in software work.

You’ll then bring these principles together by shaping an AI-assisted workflow for planning and design tasks.

By the end, you’ll understand how to apply generative AI thoughtfully across the early stages of the SDLC and be ready to progress into AI-assisted coding, testing, and debugging.

This course is designed for software engineers, web and application developers, technical leads, QA and DevOps professionals who want to apply generative AI across early SDLC tasks. Programming experience is required; prior AI knowledge is not.

Learners need a computer and a stable internet connection to follow the course demonstrations and explore AI-assisted development workflows. The course introduces tools such as Cursor, Tabnine, Copilot Agent Mode, and Snyk. Specific operating system requirements, installation requirements, account requirements, and licensing details are not fully specified in the course material and should be confirmed before finalising the setup instructions.

This course is designed for software engineers, web and application developers, technical leads, QA and DevOps professionals who want to apply generative AI across early SDLC tasks. Programming experience is required; prior AI knowledge is not.

• Explain the fundamentals of generative AI, large language models, and their applications across the software development lifecycle.
• Apply prompt engineering techniques, including context structuring, constraints, and iterative refinement, to technical development tasks.
• Create early-SDLC artefacts such as refined requirements, user stories, specifications, acceptance criteria, and design options using AI-assisted workflows.
• Develop verification strategies for reviewing and refining AI-generated outputs before they influence software design or implementation decisions.
• Evaluate AI-assisted development workflows using verification practices, guardrails, and human oversight to support responsible AI use.