Building and Deploying Full-Stack Apps with AI Assistance

Last month I built a task manager app from scratch in a single weekend. Not a toy demo, but a full-stack application with authentication, a PostgreSQL database, a REST API, and a React frontend. The secret? I used AI as my co-developer throughout the entire lifecycle. Here’s exactly how I did it, and what I learned along the way.

Phase 1: Architecture Planning

Before writing a single line of code, I spent 30 minutes with AI planning the architecture. This is the step most people skip, and it’s the most valuable. I described my requirements in plain language and asked for an architecture recommendation.

The key prompt I used was something like: “I need a task manager with user auth, teams, task assignment, due dates, and status tracking. Suggest a tech stack and database schema for a solo developer who needs to ship fast.”

The AI suggested a stack I was already comfortable with (Node.js, Express, React, PostgreSQL) but proposed a database schema I hadn’t considered. It suggested a separate task_assignments junction table instead of a simple assigned_to column, which made multi-assignee support trivial later on.

Phase 2: Database and API Scaffolding

With the schema agreed upon, I had AI generate the SQL migrations:

CREATE TABLE users (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email VARCHAR(255) UNIQUE NOT NULL,
  password_hash VARCHAR(255) NOT NULL,
  display_name VARCHAR(100) NOT NULL,
  created_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE teams (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  name VARCHAR(100) NOT NULL,
  owner_id UUID REFERENCES users(id) ON DELETE CASCADE,
  created_at TIMESTAMP DEFAULT NOW()
);

CREATE TABLE tasks (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  team_id UUID REFERENCES teams(id) ON DELETE CASCADE,
  title VARCHAR(255) NOT NULL,
  description TEXT,
  status VARCHAR(20) DEFAULT 'todo' CHECK (status IN ('todo', 'in_progress', 'review', 'done')),
  priority INTEGER DEFAULT 0,
  due_date TIMESTAMP,
  created_by UUID REFERENCES users(id),
  created_at TIMESTAMP DEFAULT NOW()
);

Then I asked for the Express routes. Rather than generating everything at once, I went resource by resource: auth routes first, then teams, then tasks. Each time I reviewed the output, adjusted naming conventions, and asked for corrections.

Phase 3: API Route Implementation

For each route, I followed a pattern: describe what the endpoint should do, have AI generate the implementation, then review it critically. Here’s what a typical generated route looked like after my review and edits:

router.post('/tasks', authenticate, async (req, res) => {
  const { title, description, team_id, due_date, priority } = req.body;

  if (!title || !team_id) {
    return res.status(400).json({ error: 'Title and team_id are required' });
  }

  // Verify user belongs to team
  const membership = await db.query(
    'SELECT 1 FROM team_members WHERE team_id = $1 AND user_id = $2',
    [team_id, req.user.id]
  );

  if (membership.rows.length === 0) {
    return res.status(403).json({ error: 'Not a member of this team' });
  }

  const result = await db.query(
    `INSERT INTO tasks (title, description, team_id, due_date, priority, created_by)
     VALUES ($1, $2, $3, $4, $5, $6) RETURNING *`,
    [title, description, team_id, due_date, priority || 0, req.user.id]
  );

  res.status(201).json(result.rows[0]);
});

Notice the authorization check. AI included it because I mentioned in my project context that all task operations require team membership verification. Setting up that context upfront saved me from having to request it for every endpoint.

Phase 4: Frontend Components

For the React frontend, I had AI generate components in order of dependency: layout shell first, then the task board, then individual task cards. I gave it my design tokens and existing component patterns so the output matched my project’s style.

The most useful technique was generating the data-fetching hooks separately from the UI components. This made testing straightforward because I could mock the hooks independently.

Phase 5: Deployment

Deployment is where AI assistance really shines for reducing friction. I asked for a Docker Compose configuration for production, a Nginx reverse proxy config, and a GitHub Actions workflow for CI/CD. Each of these would have taken me 30-45 minutes of documentation reading. With AI, I had working configs in minutes and spent my time reviewing them instead.

Lessons From the Full Cycle

After completing the project, here are my honest takeaways:

• Planning time is amplified. Every minute spent on architecture planning saved 10 minutes of implementation. AI makes bad architecture easier to build too, so think before you prompt.
• Go resource by resource. Generating an entire API at once produces inconsistencies. Building one resource at a time, with review between each, gives much better results.
• AI doesn’t replace testing. I still wrote integration tests for every endpoint. AI generated the test boilerplate, but I defined the assertions myself.
• Context compounds. As I built more of the app, I could reference earlier decisions. “Follow the same pattern as the teams routes” produced increasingly accurate output.
• Deployment configs need careful review. AI-generated Docker and Nginx configs often have subtle issues around environment variables, volume mounts, and SSL termination. Always test these in staging first.

The total build time was about 14 hours across a weekend. Without AI assistance, I’d estimate the same project at 30-40 hours. That’s not because AI wrote all the code. It’s because AI eliminated the friction between knowing what I wanted and getting it on screen.