Diploma in AI-Assisted Programming & Software Development

Understand how software projects are planned, built, reviewed, and shipped

Use AI assistants to explain concepts, compare approaches, and generate practice tasks

Learn to verify AI suggestions instead of accepting code blindly

Set up editor, terminal, Git, package manager, and browser dev tools

Build a disciplined weekly coding practice routine

Use variables, data types, functions, loops, arrays, objects, and modules

Break problems into smaller steps before writing code

Write readable code with naming, structure, and comments where useful

Debug syntax, runtime, and logic errors methodically

Complete small algorithm and data-handling exercises

Build reusable components, props, state, and event-driven interfaces

Create forms, validation, loading, empty, and error states

Style responsive layouts with a consistent design system

Use accessibility basics for labels, focus, keyboard, and contrast

Build a polished front-end feature from a product brief

Understand client/server responsibilities and request lifecycle

Create server routes and business logic safely

Handle validation, errors, authentication concepts, and permissions

Structure code so features remain maintainable as they grow

Build a server-backed workflow such as enquiries, bookings, or submissions

Model users, courses, orders, enquiries, or tasks using relationships

Write SQL queries for create, read, update, delete, filtering, and reporting

Apply constraints, validation, and data quality thinking

Connect application forms to persistent storage patterns

Design a small production-style database schema

Design REST-style endpoints with clear payloads and status codes

Consume external APIs and handle failures gracefully

Manage secrets, environment variables, and integration configuration

Document APIs so other developers can use them

Integrate a practical service such as messaging, email, payment, or analytics conceptually

Prompt AI for plans, edge cases, tests, and alternative implementations

Review generated code for correctness, security, performance, and maintainability

Use AI to understand unfamiliar codebases and error logs

Refactor duplicated or fragile code with confidence

Create a personal checklist for responsible AI-assisted development

Write unit, integration, and browser-flow test concepts

Test forms, APIs, database interactions, and user journeys

Use logs, stack traces, browser tools, and reproduction steps

Create release checklists for responsive design and accessibility

Fix defects based on evidence rather than guessing

Understand environments, builds, hosting, domains, and deployment workflows

Use environment variables and configuration safely

Prepare production checks for performance, security, and errors

Monitor basic application health and collect user feedback

Deploy a capstone application with a release note

Define a real problem, users, scope, and success criteria

Plan features, data model, UI flows, API routes, and test cases

Build an end-to-end web application using AI responsibly as a coding assistant

Document architecture, trade-offs, known limitations, and next steps

Present the deployed project as a portfolio-ready case study