AI-Assisted Software Development: From Requirements to Delivery

Chapter 1: Foundations of Al-Assisted Software Development

•  This chapter introduces the shift from traditional software development to Al-assisted development workflows. You will learn how Al tools support different stages of development,
  how to select the right tool for the task, and when not to rely on Al. The chapter establishes the end-to-end workflow used throughout the course: intent, requirements, architecture, data, coding, validation, and delivery.

Chapter 2: Al-Assisted Requirements Analysis and Validation

•  This chapter focuses on using Al to analyze stakeholder inputs and turn them into usable requirements. You will learn how to extract requirements, assumptions, gaps, risks, contradictions, dependencies, and clarification questions from unstructured source material, You will also learn how to validate AI-generated requirements against the original sources and create a traceable backlog before architecture work begins.

Exercise: Create a Validated Requirements Backlog

Use Al to analyze stakeholder inputs, compare tool outputs, identify gaps and contradictions. and create a traceable requirements backlog

Chapter 3: Al-Assisted Architecture and Design

•  This chapter teaches you how to translate validated requirements into architecture and design plans. You will learn how to create architecture prompts that include scale, budget, security, compliance, technology constraints, and tradeoffs. You will also compare architecture options, identify risks, validate traceability, and prepare architecture outputs for implementation planning.

Exercise: Move from Architecture to Implementation Planning

Use Al to translate validated requirements into architecture components, implementation- support artifacts, and a validated design-to-build workflow.

Chapter 4: Data Foundations, RAG, and Prompt Systems

•  This chapter explains why data quality directly affects Al reliability. You will learn how Retrieval- Augmented Generation connects Al tools to enterprise data, how repeatable data pipelines support reliable Al outputs, and how structured prompt systems improve consistency. You will also learn how to break complex Al tasks into validated workflows instead of relying on one large prompt.

Exercise: Build a RAG-Ready Data and Prompt Workflow

Use Al to assess data quality, identify retrieval risks, design a repeatable data pipeline, and create a structured prompt workflow.

Chapter 5: Agentic Coding, Delivery, Governance, and Scaling

•  This chapter brings the full workflow into a controlled coding and delivery process. You will compare simp le prompting with agent-based coding workflows, use Al tools to plan, build, test review, and document code changes, and apply validation checkpoints before delivery.
•  You will also examine CI/CD readiness, prompt and output tracking, security, governance, cost, monitoring, and scaling considerations for Al-assisted development

Exercise: Plan, Build, Test, and Review Code with Al

Use Al tools to plan a coding task, make controlled code changes, run or review tests, document the workflow, and make a delivery decision.

Hands-On Exercises

Throughout the 1-day class, participants complete four guided hands-on exercises that follow the Al- assisted development lifecycle.

Together, these hands-on exercises give attendees practice using Al across requirements, architecture, data, coding, testing, validation, and governance.