AI Migration Generator

Have you ever written the migration and then realised you had no idea how to undo it? Adding a column is easy. Splitting one table into two, backfilling the data, swapping the foreign key and keeping the application running throughout is the part that gets people. A migration is code that runs once against real data, and that makes it the least forgiving code in the repository.

What is AI Migration Generator?

It is a free page that writes database change scripts. You describe the current state, describe the state you want, and it produces the statements that get you from one to the other, plus the statements that put you back if the deploy goes wrong.

Scope matters here. This is not a diffing tool wired into your database. It reads your description, so the quality of the migration tracks the quality of what you tell it. Paste the current CREATE TABLE and say what should change, and the result is usually close to ready. Say "add tags to posts" with no context and you will get a reasonable guess that may not match your conventions.

Why Use AI Migration Generator?

Because the risky migrations are the ones you write rarely. Adding a nullable column is muscle memory. Renaming a column that six services read, or converting a text field into a foreign key, comes up twice a year, and the safe pattern for it is a multi step dance most people have to look up.

AI Migration Generator is useful there. It knows the expand and contract pattern, it knows why you add the new column before you stop writing the old one, and it will lay the steps out in order. You still make the call about what is safe on your system. What you save is the hour of remembering how this is normally done.

How Does AI Migration Generator Work?

The page is a single column. A prompt box takes the description, a selector picks which AI model reasons about it, an accordion holds the database settings, and the result lands in a card with a copy button on the code block and a live word count in the footer.

The generation itself is one pass, but the useful workflow is two. Generate the migration, read it, then send it back through with the reuse button and ask for the version that is safe on a table with millions of rows. The second answer is usually the one you ship.

It helps to know in advance which category your change falls into, because that decides how much detail you need to give.

Step-by-Step Guide

1. Open AI Migration Generator. There is no sign in step and nothing to install.
2. Paste the current definition of the tables involved. This single habit removes most of the guesswork.
3. State the target state in one or two sentences, including what should happen to existing rows.
4. Choose a model. Anthropic Claude AI, Google Gemini, Qwen, NVIDIA AI and others are on the selector.
5. In advanced options, set Database to your engine and Output Type to Migration.
6. Turn Add Comments on so each step carries its reason, and turn Add Constraints on if the change touches keys.
7. Generate, read both directions of the migration, then take it away with copy or the DOC, TXT and HTML exports underneath.

Anything you generated earlier in the session stays in the activity history panel below the result, which is handy when you want to compare the cautious version against the quick one.

Key Features

Best Use Cases

If the change starts from a model rather than an existing table, generate the target schema first with AI Database Schema Generator and then describe the difference here.

Advanced Options Guide

Ten controls sit behind the accordion. For migrations, Database and Output Type are the two that must be right, and Custom Instructions is where the safety requirements go.

Example Inputs

Here is the kind of prompt that produces a usable answer. The case runs through the rest of this article: an application stores a customer's country as free text and you want it to become a foreign key to a countries table.

Current table:
CREATE TABLE customers (
  id BIGSERIAL PRIMARY KEY,
  name TEXT NOT NULL,
  country TEXT
);

Target: country should become country_id referencing a new countries table
(id, iso_code UNIQUE, name). Existing country text values must be matched
to the new table by name. customers has about 3 million rows and cannot be
locked for more than a second. PostgreSQL 15.

Every sentence there does work. The current definition removes guesswork, the row count changes the strategy, and the locking rule rules out the simple version.

Example Outputs

With Output Type on Migration, Complexity on Advanced and comments on, the answer comes back staged rather than as one statement.

-- Step 1: create the lookup table and seed it from existing values
CREATE TABLE countries (
  id       BIGSERIAL PRIMARY KEY,
  iso_code VARCHAR(2) NOT NULL UNIQUE,
  name     TEXT       NOT NULL
);

-- Step 2: add the new column as nullable so no rewrite is needed
ALTER TABLE customers ADD COLUMN country_id BIGINT;

-- Step 3: backfill in batches, keeping each transaction short
UPDATE customers c SET country_id = co.id
FROM countries co
WHERE c.country = co.name AND c.country_id IS NULL
  AND c.id IN (SELECT id FROM customers
               WHERE country_id IS NULL ORDER BY id LIMIT 10000);

-- Step 4: only after the application writes both columns
ALTER TABLE customers
  ADD CONSTRAINT customers_country_fk
  FOREIGN KEY (country_id) REFERENCES countries(id) NOT VALID;
ALTER TABLE customers VALIDATE CONSTRAINT customers_country_fk;

The interesting choices are the cautious ones. The column is added nullable, because a NOT NULL default would rewrite three million rows. The backfill is bounded, so each transaction finishes quickly. And the foreign key is added as NOT VALID then validated separately, which is the PostgreSQL specific trick that keeps the table available while the check runs.

Tips & Common Mistakes

Work down this before the migration goes anywhere near production.

• ✅ Current table definitions pasted into the prompt, not summarised.
• ✅ Row counts stated for every table the migration touches.
• ✅ Down migration read line by line, and its data loss understood.
• ✅ Backfill batched if the table is large, with a way to see progress.
• ✅ Whole migration rehearsed on a restored copy of production.
• ✅ Deploy order agreed, since application code and schema change separately.

The usual failures are these. Treating a generated down migration as a real rollback when it drops a column and takes the data with it. Adding a NOT NULL column with a default on a big table and locking it for minutes. Applying a unique constraint without first checking for duplicates. And running the whole thing in one transaction on an engine where a long transaction blocks everything else.

AIToolsay builds a separate page for each job so the tool already knows what kind of answer you want. On this one the prompt box expects a schema change, the options carry a dialect setting and a complexity dial that maps to how careful the migration should be, and the model selector lets a second engine review the same plan. Everything is free and open, with no account between you and the first result, and the session history keeps every version listed underneath while you compare a cautious sequence against a quick one. The rest of the development tooling on AIToolsay is arranged the same way, so the schema you are migrating toward and the queries that will read it are both a page away.

Frequently Asked Questions

Take the next schema change on your list, paste the current tables, and state the row counts honestly. The answer changes noticeably once the tool knows how big the table is. If migrations are a regular part of your week, the Telegram community is a good place to compare approaches, and the newsletter or push notifications will tell you when new database tools appear here.

Let AI Speak.