AI-First Development & Workflows — Web Development

Welcome to the new era of software engineering. For decades, developers have manually written every line of code, configured every webpack file, and painstakingly traced every bug. Today, Artificial Intelligence acts as a senior pair programmer that never sleeps. In this masterclass, we will teach you from the ground up how to shift your mindset from being a 'code writer' to a 'systems director'.

Module 1: The Basics — What is an AI Workflow?

Before touching any code, we must understand how Large Language Models (LLMs) operate. An LLM predicts the next token based on the context you provide. An 'Agentic Workflow' means the AI isn't just generating text; it is given tools (like terminal access, file system access) and a loop to plan, execute, evaluate, and correct its actions.

The Evolution of Coding

Phase 1: Manual Coding (Writing everything from scratch).
Phase 2: Snippet Auto-completion (Basic GitHub Copilot).
Phase 3: Agentic Architecture (AI generates the folder structure, configures the build system, and writes the test suite automatically).

Module 2: Advanced Prompt Engineering for Developers

The quality of the AI's output is directly proportional to the quality of your input (the prompt). "Write a React component" is a terrible prompt. Let's look at the anatomy of an expert-level prompt.

ROLE: Act as a Staff-level React Performance Engineer.

CONTEXT: We are building a high-frequency trading dashboard that receives 100 WebSocket messages per second. The current implementation freezes the UI.

TASK: Refactor the `OrderBook` component to handle this throughput without dropping frames.

CONSTRAINTS:
1. You MUST use React 18 concurrent features (`useTransition`, `useDeferredValue`).
2. Do NOT use any external state management libraries like Redux.
3. The code must be strictly typed with TypeScript.

OUTPUT FORMAT:
Provide only the refactored code block with comments explaining the performance optimizations. Do not output introductory text.

Module 3: Configuring Your AI-Native IDE

To teach from scratch, let's set up VS Code (or an AI-first fork like Cursor) properly. AI tools need context.

{
  // Enable advanced inline suggestions
  "github.copilot.enable": {
    "*": true,
    "plaintext": false,
    "markdown": true
  },
  // Configure context inclusion
  "ai.context.include": [
    "src/types/**/*.ts",
    "src/components/**/*.tsx",
    "architecture.md"
  ]
}

Always keep a .cursorrules or architecture.md file in your project root. When you ask the AI a question, it will automatically read this file to understand the project's 'ground rules' (e.g., "Always use Tailwind CSS", "Never use any in TypeScript").

Module 4: Scaffold Complex Architectures in Seconds

Let's put the AI to work. Instead of spending 4 hours setting up a Monorepo, we will instruct an agent to do it.

# The AI will generate and run a script like this based on your prompt
npx create-turbo@latest my-monorepo --use-npm
cd my-monorepo
npm install @types/node -w packages/ui
# It will then create the exact folder structures for micro-services

Module 5: Local, Private AI Models with Ollama

Enterprise companies often forbid sending proprietary code to OpenAI or Anthropic. You must learn to run models locally on your own GPU.

Step 5.1: Install Ollama (a tool that runs LLMs locally).

curl -fsSL https://ollama.com/install.sh | sh

# Pull 'codellama' (a model specifically trained on code)
ollama run codellama:7b

Step 5.2: Install the 'Continue' extension in VS Code. Configure it to point to your local machine.

{
  "models": [
    {
      "title": "Local CodeLlama",
      "provider": "ollama",
      "model": "codellama:7b",
      "apiBase": "http://localhost:11434"
    }
  ]
}

Now, your code never leaves your laptop, ensuring 100% compliance with security policies.

Module 6: AI-Driven Test Generation

Writing tests manually is a poor use of an expert's time. Let's see how an AI generates an exhaustive test suite.

export function validatePasswordStrength(pwd: string): boolean {
  const hasUppercase = /[A-Z]/.test(pwd);
  const hasNumber = /[0-9]/.test(pwd);
  const isLongEnough = pwd.length >= 8;
  return hasUppercase && hasNumber && isLongEnough;
}

By prompting: "Generate Vitest unit tests for this function covering all edge cases", the AI produces:

import { validatePasswordStrength } from './auth';
import { describe, it, expect } from 'vitest';

describe('validatePasswordStrength', () => {
  it('returns true for a valid password', () => {
    expect(validatePasswordStrength('StrongPwd123')).toBe(true);
  });

  it('returns false if too short', () => {
    expect(validatePasswordStrength('Str1')).toBe(false);
  });

  it('returns false if missing uppercase', () => {
    expect(validatePasswordStrength('weakpwd123')).toBe(false);
  });

  it('returns false if missing number', () => {
    expect(validatePasswordStrength('StrongPassword')).toBe(false);
  });

  it('handles empty strings securely', () => {
    expect(validatePasswordStrength('')).toBe(false);
  });
});

Module 7: Automated CI/CD AI Reviewer

The final stage of mastery is integrating AI into your continuous integration pipeline. This prevents bad code from ever reaching the main branch.

name: AI PR Reviewer

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run AI Reviewer
        uses: coderabbitai/coderabbit-action@v1
        with:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          system_prompt: "You are a strict AppSec engineer. Only comment if you find SQL injection, XSS, or hardcoded secrets. Ignore formatting."