AI Coding with
Flask

Why simple stacks win in the age of AI-assisted development

In 2026, AI writes half your code. Choose a stack that AI understands.

Claude, ChatGPT, and GitHub Copilot are incredible at writing Flask + PostgreSQL + Vanilla JS. They're mediocre at writing Next.js + Prisma + TypeScript.

Why? Because simple, explicit code is easier to generate and easier to verify. When you can read the SQL query, you know it's correct. When you see raw JavaScript, you know what it does.

AI coding isn't about writing less code. It's about writing verifiable code.

The AI Feedback Loop Test

Same task, different stacks. Based on building real projects — not a controlled study.

Flask + psycopg2 + Vanilla JS

95%

First-Try, No Iteration Needed

SQL is explicit — read it and know it's correct

Routes are simple and predictable

JavaScript is vanilla (no JSX/transpilation)

No build step — refresh and see

Errors are in your code, not the framework

Next.js + Prisma + TypeScript

45%

Usually Needs 2–3 Iterations

Prisma query hides SQL — must run to verify

TypeScript types often misaligned

Server/client boundary causes silent bugs

Build step adds 30–60s per iteration

Stack traces span framework internals

The Honest Take

These numbers are based on experience building real projects — not a rigorous controlled study. Reasonable people can debate the exact percentages. But the underlying cause is structural: Flask output is verifiable by reading it. Prisma output requires running it, checking SQL logs, and trusting an abstraction layer.

AI models are getting significantly better at TypeScript and Next.js every few months. The success rate gap will narrow. The verification gap will not. That's the durable argument.

Side-by-Side: Flask vs Next.js

Same feature. Radically different complexity.

Task: Fetch Users from Database

Flask + psycopg2 (8 lines)
@app.route('/users')
def get_users():
    cur.execute("""
        SELECT id, name, email
        FROM users
        WHERE active = true
    """)
    users = cur.fetchall()
    return render_template('users.html', users=users)

Crystal clear. You see the exact SQL.

Next.js + Prisma (25+ lines)
// app/users/page.tsx
import { prisma } from '@/lib/prisma'

export default async function UsersPage() {
  const users = await prisma.user.findMany({
    where: { active: true },
    select: { id: true, name: true, email: true }
  })

  return (
    <div>
      {users.map(user => (
        <UserCard key={user.id} user={user} />
      ))}
    </div>
  )
}

// What SQL does Prisma generate? 🤷
// Does it use indexes? 🤷
// How do you verify it? Run it and check logs.

Hidden SQL. TypeScript overhead. JSX complexity.

Task: Create New User (with validation)

Flask + psycopg2 (15 lines)
@app.route('/users', methods=['POST'])
def create_user():
    email = request.form['email']
    name = request.form['name']

    # Validation
    if not email or '@' not in email:
        return "Invalid email", 400

    # Insert
    cur.execute("""
        INSERT INTO users (email, name)
        VALUES (%s, %s)
        RETURNING id
    """, (email, name))
    conn.commit()
    return redirect('/users')

Explicit validation. Visible SQL. Easy to debug.

Next.js + Prisma (40+ lines)
// app/api/users/route.ts
import { prisma } from '@/lib/prisma'
import { z } from 'zod'

const userSchema = z.object({
  email: z.string().email(),
  name: z.string().min(1)
})

export async function POST(req: Request) {
  try {
    const body = await req.json()
    const { email, name } = userSchema.parse(body)

    const user = await prisma.user.create({
      data: { email, name }
    })

    return Response.json(user, { status: 201 })
  } catch (error) {
    if (error instanceof z.ZodError) {
      return Response.json(
        { errors: error.errors },
        { status: 400 }
      )
    }
    return Response.json(
      { error: 'Server error' },
      { status: 500 }
    )
  }
}

// Plus client-side form with React Hook Form
// Plus TypeScript types
// Plus error handling UI
// Plus loading states...

Zod schema + Prisma schema + TypeScript types. So much indirection.

Task: Display User List

Flask Template (Jinja2)
<table>
  <thead>
    <tr>
      <th>Name</th>
      <th>Email</th>
    </tr>
  </thead>
  <tbody>
    
  </tbody>
</table>

Pure HTML. View Source shows exactly this.

Next.js Component (React)
// components/UserTable.tsx
import { User } from '@/types'

interface Props {
  users: User[]
}

export default function UserTable({ users }: Props) {
  return (
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Email</th>
        </tr>
      </thead>
      <tbody>
        {users.map((user) => (
          <tr key={user.id}>
            <td>{user.name}</td>
            <td>{user.email}</td>
          </tr>
        ))}
      </tbody>
    </table>
  )
}

JSX + TypeScript interface + separate @/types import. Structure for its own sake at this scale.

The Pattern

Flask code is 3-5x shorter and infinitely more verifiable. When AI generates a Flask route, you read it and know it works. When AI generates Next.js code, you have to run it, check TypeScript errors, verify Prisma queries, and pray the build succeeds.

AI Coding Workflow Comparison

How development actually happens in 2026

Flask Workflow

  1. 1.

    Write prompt

    "Create a Flask route to fetch active users"

  2. 2.

    AI generates code

    8 lines of Python with raw SQL

  3. 3.

    Read and verify

    SQL is visible → looks correct → done

  4. 4.

    Refresh browser

    No build step. Instant feedback.

  5. 5.

    Ship it

    Total time: 2 minutes

Next.js Workflow

  1. 1.

    Write detailed prompt

    "Create Next.js API route with Prisma, TypeScript types..."

  2. 2.

    AI generates code

    40+ lines across multiple files

  3. 3.

    Fix TypeScript errors

    Types are wrong, imports missing

  4. 4.

    Wait for build

    30-60 seconds...

  5. 5.

    Runtime error

    Prisma query is wrong, fix and rebuild

  6. 6.

    Check generated SQL

    Enable Prisma logging, verify query

  7. 7.

    Finally works

    Total time: 15-20 minutes

Flask: 2 minutes. Next.js: 20 minutes. 10x faster development.

Want the full side-by-side code breakdown? See the Flask vs Next.js comparison →

Practical AI Prompts for Flask

Copy these prompts and get working code on the first try

CRUD Operations

Create a Flask route to display all users from PostgreSQL using psycopg2. - Query: SELECT id, name, email, created_at FROM users WHERE active = true - Template: users.html - Add pagination (20 per page) - Include error handling

Authentication

Create Flask login/logout routes with session-based auth using psycopg2. - Hash passwords with bcrypt - Store sessions in Flask session (secret key) - Login route: POST to /login with email/password - Check credentials against users table - Logout route: clear session - Add @login_required decorator

File Upload

Create a Flask route to handle image uploads. - Accept POST to /upload with multipart form - Validate: only PNG/JPG, max 5MB - Save to /static/uploads/ with unique filename - Store filepath in database: INSERT INTO images (user_id, filepath) - Return JSON with image URL

API Endpoint

Create a Flask API endpoint that returns user statistics as JSON. - GET /api/stats - Query PostgreSQL for: total users, active users (last 30 days), new signups (this week) - Return JSON: {"total": 1234, "active": 890, "new_this_week": 45} - Add caching (10 minutes)

Search Feature

Create a Flask search route with PostgreSQL full-text search. - GET /search?q=query - Use PostgreSQL ILIKE for simple search on products table (name, description) - Return matching products as JSON - Limit to 50 results - Include relevance scoring

Email Notification

Create a Flask function to send email using SMTP. - Use smtplib (Gmail SMTP) - Function: send_email(to, subject, body) - Read SMTP credentials from environment variables - Add HTML email template support - Include error handling for failed sends

Pro Tip

The key to successful AI prompts with Flask: be explicit about the tech stack. Always mention "psycopg2" (not SQLAlchemy), "Jinja2 templates" (not React), and "vanilla JavaScript" (not frameworks). AI will generate simpler, more reliable code.

Ready to Code with AI?

Start using Flask with AI assistants and experience 10x faster development.