AI is changing how software is planned, designed, built, tested, and delivered. This hands-on course teaches you how to use AI tools across the entire software development lifecycle, while maintaining structure, validation, governance, and human oversight. In this hands-on course, you will learn how to move from business intent to validated requirements, architecture, data workflows, agentic coding, testing, and delivery readiness. You will work with tools such as ChatGPT, Claude, Codex, GitHub Copilot Agent Mode, Claude Code, and Windsurf AI to understand where each tool fits, when to use it, and when not to rely on it. You will practice building AI-assisted workflows that reduce rework, improve traceability, and help teams move faster without sacrificing quality, security, or control. Through guided hands-on exercises, you will use AI to analyze stakeholder inputs, create validated backlogs, translate requirements into architecture, design RAG-ready data and prompt workflows, and manage AI-assisted coding changes through testing, review, and governance checkpoints. AI-Assisted Software Development: From Requirements to Delivery Benefits In this course, you will learn how to: Explain how AI changes the software development workflow. Choose the right AI tool for each development task. Use AI to extract and validate software requirements. Translate validated requirements into architecture and design plans. Design reliable data, RAG, and prompt workflows. Use AI agents to plan, build, test, and review code. Track AI prompts, outputs, code changes, and validation results. Apply governance, security, cost, CI/CD, and scaling controls. Prerequisites Basic familiarity with software development concepts is helpful. Prior experience with AI coding tools is not required. AI-Assisted Software Development Training Outline Learning Objectives 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 simple 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.
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