SQL

AI apps need persistent storage for users, conversations, and metadata. SQL databases provide reliable, queryable storage.

SQL is Excel for databases — rows and columns, but queries scale to millions of records instantly.

Visual Workflows

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Overview

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Key Takeaways

1.SQL is the language for relational databases — store users, chat history, and app metadata in structured tables.
2.Tables have rows and columns.
3.SELECT reads, INSERT creates, UPDATE modifies, DELETE removes.
4.PostgreSQL is the go-to for AI apps.

Real Example

Scenario

Your chat app needs to remember the last 10 messages for context window management.

What you would do

Store messages in a PostgreSQL table with chat_id, role, and content. Query: SELECT * FROM messages WHERE chat_id = 1 ORDER BY id DESC LIMIT 10.

Practice Task

Open sqlite3 (or any SQL client) and run: CREATE TABLE notes (id INTEGER, text TEXT); INSERT INTO notes VALUES (1, 'hello'); SELECT * FROM notes; UPDATE notes SET text='world' WHERE id=1; DELETE FROM notes WHERE id=1;

Commands

Commands to Remember

SELECT * FROM table WHERE condition; # read rows matching a condition
INSERT INTO table (col) VALUES ('val'); # add a new row
UPDATE table SET col = 'val' WHERE id = 1; # modify an existing row
DELETE FROM table WHERE id = 1; # remove a row
psql -U user -d dbname # connect to a PostgreSQL database

Cheat Sheet

Quick recap

quick ref

•SELECT read · INSERT create · UPDATE modify · DELETE remove
•Data stored in tables — rows and columns with defined schema
•PostgreSQL is the default for AI apps
•JOIN combines related tables · indexes speed up queries
•SQLAlchemy maps Python to tables · Alembic handles migrations
•pgvector adds embedding search inside Postgres

Common Mistakes

✕No database migrations — schema drift
✕SQL injection from raw string queries
✕Not indexing frequently queried columns