AI-Assisted Development: A Practical Introduction | Claude

You already know how to build software. Now you want to understand where AI can help, what to tell it, and how to judge the work it produces.

AI-Assisted Development: A Practical Introduction is a free, focused introduction for existing developers across languages and frameworks. In less than an hour, you will explore the wider development landscape, then follow small, real demonstrations with ChatGPT, Claude Code and GitHub.

What does AI-assisted development mean in practice?

AI assistance reaches across requirements, codebase exploration, design, implementation, testing, debugging, review, release and maintenance. We will connect those activities to familiar development situations and explain the difference between a conversation about code, an editor suggestion and a coding agent working with project files.

A conceptual booking example illustrates the wider lifecycle. The practical work then uses a deliberately small Developer Notes Search repository, so you can follow the request, the tool actions and the result.

How could AI change your work as a developer?

We will discuss why these tools emerged, what they can help with today and the direction of more agentic workflows. You will examine the difference between automating a task and taking responsibility for a complete result, including uncertainty around changing roles and hiring.

You will also identify useful preparation priorities: keep your engineering skills active, describe intended behavior clearly, supply relevant context and learn to verify the actual work.

How will we use ChatGPT and Claude Code?

We begin with a short ChatGPT conversation about supplied code. You will see how adding the actual input, expected behavior and constraints makes the request more useful. We then inspect what the answer establishes, what still depends on the surrounding project, and what a local run shows.

Next, Claude Code works in a local clone of the course GitHub repository. You will follow it as it explores the files, traces the relevant behavior and makes a focused code change. Throughout the demonstration, we distinguish an explanation, an applied edit and evidence from running the code.

What practical result will you see?

The same project continues through two connected improvements:

• A focused search fix that ignores surrounding whitespace while preserving the existing matching behavior.

• A command-line feature that accepts your own search query, reuses the reviewed function and displays matching note titles.

For the feature, we define acceptance criteria, ask for a plan before editing, inspect the implementation and try useful queries alongside no-match, whitespace-only and missing-input cases. We rerun the supplied checks, review the changed files and staged diff, then save the accepted result and inspect its commit on GitHub.

You leave with a complete small example of a request, context, plan, code change, checks and review working together. The supplied source, prompts and reference notes help you practise that workflow yourself.

How does this connect to enterprise development?

The closing lecture maps the small demonstration to work across interfaces, APIs, data, permissions and teams. It introduces the purpose of specifications, architecture, context engineering, repository instructions, session continuity and supervised agent workflows.

It also places skills, hooks and MCP alongside testing, debugging, security, team review, delivery and monitoring. This is an introductory map of those responsibilities. Building confidence across connected systems takes further learning, repeated practice, failure investigation and review feedback.

How is the course organized?

• Getting Oriented: the AI development landscape, developer roles and preparation.

• Talking to AI: useful context, focused requests and checking ChatGPT answers.

• AI in a Repository: project exploration and a bounded change with Claude Code.

• Review and Next Steps: Git review, a usable feature and the wider enterprise workflow.

Who is this course for, and what do you need?

The course is for existing backend, frontend, full-stack and mobile developers, plus QA and automation engineers who write code. Experience with source code, project changes and basic Git is assumed. Prior AI or machine-learning knowledge is not required; the short Python examples do not require advanced Python skills.

Practising the repository demonstrations requires paid Claude Code access, such as Claude Pro. ChatGPT Free and GitHub Free are sufficient for their parts. You will use Claude Code CLI, Git, Python 3 and a code editor; the setup notes cover supported operating systems.

Bring one familiar development task to mind. By the end, you will have a clearer way to choose a manageable first attempt, explain the intended result and inspect whether the assistant's work supports it.