AI coding help — questions so production actually holds

AI coding help gave you a slick preview, then production collapsed — because the prompts never asked what happens after publish.

You searched for ai coding help, opened Lovable, Cursor, Replit Agent, or v0, and described the product. The assistant delivered screens, components, and happy-path demos. Login worked on localhost. Stripe accepted the test card in the sandbox. You published and the live URL broke on auth, empty env vars, or open database reads. The code was not wrong. The questions were incomplete.

What broken ai coding help looks like after launch

Symptoms repeat across tools.

• White screen or spinner on live URL. Missing VITE_ or NEXT_PUBLIC_ vars on the host.
• Login loops. Supabase Site URL still localhost. See lovable login for the fix path.
• Paid checkout, empty database. No live Stripe webhook registered.
• Data visible to strangers. Tables without RLS; anon key treated like a password.
• Agent refactored unrelated files. Credits gone, deploy still misconfigured.
• Same error after ten prompts. Each retry widened scope instead of fixing URLs or secrets.

The lovable deploy hub names five settings editors skip. Good ai coding help asks about those before gradient buttons.

Why ai coding help defaults to preview-only answers

Models optimize for immediate visible success in the environment shown in the chat. That is preview origin, dev secrets, and demo data. They rarely open Supabase Authentication → URL Configuration without you asking.

Tutorials reinforce UI-first flow: build pages, add auth later, deploy last. Assistants mirror that order. “Add Google login” generates client code and provider placeholders. It does not list every redirect URI you must register in Google Cloud and Supabase for production.

Wide prompts authorize wide edits. “Make it production ready” triggers dependency bumps and folder renames. Narrow production gaps stay open.

Tool boundaries matter. AI inside Lovable cannot click your Vercel env panel. AI in Cursor cannot issue TLS certificates. ai coding help that ignores those boundaries ships half a system.

Questions to ask for ai coding help before deploy

Copy these into your next session. Require checklist answers, not only code.

1. Which exact env var names does this code read? List client and server names. Ask where to set them on Vercel, Replit Deployment secrets, or Lovable connected host.
2. What is the production Site URL and every Redirect URL? Demand full https:// origins for live, preview, and localhost.
3. Which Supabase tables need RLS and what policies? Require auth.uid() examples for select, insert, update — not “we should add security later.”
4. Where does the Stripe webhook handler live and which events? Ask for live endpoint path and whsec_ placement server-side only.
5. Does any secret appear in client bundle? Reject service role keys or webhook secrets in frontend files.
6. What breaks if I only change the domain? Forces DNS, SSL, and auth URL follow-up list.
7. Single file and single error for this fix. Paste stack trace. Forbid refactors and schema resets.
8. What manual dashboard steps after your code change? If answer is none, suspect preview-only thinking.

Ask one cluster per message. Auth URLs in one thread. RLS in the next. Mixing everything invites shallow answers.

Prompt templates that survive production

Auth: “List Supabase Site URL and Redirect URLs for production at [your live URL]. Show exact dashboard clicks. Do not change unrelated files.”

Env: “List every process.env and import.meta.env name in this repo. Say which belong in Vercel Production only. No code changes.”

Payments: “Stripe checkout works in test. Give steps to register live webhook at [path] and which env var holds whsec_. Server route only.”

Database: “Enable RLS on [table]. Write policies so users read only their rows. SQL only — no UI refactor.”

Debug: “Error: [one line]. File: [path]. Minimal patch only. Do not rename folders or reset database.”

These templates turn ai coding help from feature factory into launch checklist.

Order matters. Ask dashboard questions before cosmetic prompts. Once production URLs and env are correct, UI polish prompts are cheap. Reverse order burns limits on gradients while login still loops.

Keep a personal paste file: your live URL, Supabase project ref, Vercel project name. Paste into every session so the model stops inventing localhost defaults.

When the assistant suggests “we can add that later,” push back on auth, env, and payments. Later usually means never, and fixes cost more credits after launch.

Checklist after ai coding help before you share the URL

• Live URL loads without console errors in private window
• Every env name from code exists on production host
• Supabase Site URL matches live origin
• Redirect URLs include live and preview
• RLS on and tested with anon key
• Live Stripe webhook delivers test event successfully
• No secrets in client bundle or git history
• Agent diff limited to agreed files — revert scope creep

FAQ

What should I ask for ai coding help before deploying?

Ask for production Site URL and redirect URL checklist, env var names for your host, RLS policies on user tables, and live Stripe webhook steps. UI prompts without these leave a broken live URL.

Why does ai coding help fix preview but not production?

Assistants optimize for the environment they see — localhost or tool preview. Production needs different origins, secrets on the host, and dashboard settings the AI cannot click for you.

How do I stop ai coding help loops on the same bug?

Paste one error line, name one file, forbid refactors. Fix URL and env configuration manually once, then ask for a minimal code change. Wide retry prompts burn limits without shipping.