Quick Judgment
Lovable + Supabase not working can mean several different things. The issue may be database schema, auth identity, RLS policy, storage policy, frontend state, environment configuration, or generated code that no longer matches the app's real data model.
If your Lovable app cannot read, write, log in, save records, upload files, or show the right user's data, do not keep prompting randomly. A prompt that makes data appear may also weaken the rules that protect user data. First identify the broken layer, then decide whether to patch, refactor, migrate, or rebuild. For high-risk cases, request a Supabase-focused review.
Why Lovable + Supabase failures are hard to diagnose
Lovable can create a polished prototype quickly, and Supabase can provide auth, database, storage, and edge functions. The combination is powerful, but it creates several invisible boundaries. A screen may look broken because the frontend is wrong. Or because the query is wrong. Or because the user is not authenticated. Or because RLS correctly blocks the request. Or because RLS is too permissive and the app only appears to work.
For a non-technical founder, these symptoms look similar: "the app does not save" or "the data is missing." But each root cause requires a different decision.
Database schema vs auth vs RLS vs frontend state
Database schema is the structure of your tables and relationships. If the schema is wrong, the app may not have a reliable way to represent users, teams, projects, roles, files, or payments.
Auth identifies the current user. If auth is wrong, the app may not know who is making the request, which rows belong to them, or whether they are allowed to perform an action.
RLS controls row access inside Supabase. If RLS denies everything, the app may show empty screens. If RLS allows too much, users may access data they should not see.
Frontend state controls what the UI displays. It can hide or show things, but it should not be the only permission boundary.
The safest review checks how these layers work together instead of treating one symptom as one bug.
Symptoms table
| What you see | Likely layer | Risk | | --- | --- | --- | | User logs in but sees no records | RLS or ownership columns | Valid users may be blocked | | User sees another user's records | RLS or tenant model | Data exposure risk | | Save button appears to work but data disappears | Database schema or mutation code | Lost or inconsistent data | | Upload works for one user but files are visible to others | Storage policy | File exposure risk | | App works in Lovable preview but not production | Environment or deployment | Launch reliability risk | | AI fix makes data appear by disabling checks | Permissions | Hidden production risk |
If any symptom involves user data, compare AI app database or permission problem and Supabase RLS audit before launch.
Why random prompting can expose or corrupt user data
AI may optimize for making the immediate error disappear. If the error says permission denied, AI might suggest disabling RLS or broadening a policy. If a query fails, it might change the client code to use a different identifier. If saving fails, it might add default values that corrupt the real product model.
Those changes can make a demo pass while creating production risk. The correct answer may be to fix ownership columns, adjust policy conditions, split admin and user paths, or redesign the data model. That decision requires context.
This is especially important when the app already has users, customer records, uploaded files, or paid accounts. A random prompt may run against live tables, change production policies, or create a migration that is hard to reverse. Even if no real users exist yet, repeated AI changes can make the data model harder to reason about.
The goal is not to become a Supabase expert before launching. The goal is to know which layer is responsible and which parts should not be changed casually. A good review should produce a practical sequence: inspect ownership, verify policies, test negative cases, fix the narrow issue, or rebuild the data model before launch.
What not to do next
Do not disable RLS without understanding who should access each row. Do not use service-role keys in the frontend. Do not rely on hidden buttons as security. Do not let AI rewrite schema, auth, and UI in one pass. Do not launch while you cannot explain whether users can access only their own data.
Safe next step
Freeze broad changes and identify the failing layer: database, auth, RLS, storage, frontend, or deployment. A review should tell you what is broken, what is risky, and what AI should not change next.
If your Lovable + Supabase app is close to real users or already holds data, request a Production Risk Review before another prompt changes permissions.