Foundations — The Engineering Base
FreePython for AI pipelines (types, JSON, errors, venv, Pydantic), terminal fluency (navigation, grep, PATH, secrets), Docker with sane images and .dockerignore, SQL plus vectors, and Git workflows that treat prompts and config as versioned artefacts.
What you'll build
- Python: types, Pydantic-shaped JSON, venvs, and streaming data patterns
- Terminal: navigation, grep, logs, PATH, secrets via .env / dotenv
- Docker: multi-stage images, health checks, and .dockerignore as a security boundary
- SQL + pgvector-style modelling beside semantic search
- Git: branches, commits, and treating prompts and config as versioned artefacts
Lessons
- Python Basics: Types and logic for AI
Start here if you are new to Python or need a refresher. We cover variables, f-strings, conditionals, and loops — the basic building blocks for building prompt templates and retry logic.
- Python Advanced: Data, Files and JSON
AI data lives in JSON and Lists. Learn to handle nested dictionaries, read context files, and handle the errors that crash production AI pipelines.
- Python Mastery for AI Data Processing
Python as the lingua franca of AI libs: type hints and Pydantic-shaped payloads, functions and imports, resilient JSON I/O, venv plus lockfiles, streaming patterns for JSONL — then consolidate with the CLI expense tracker.
- CLI & Terminal Mastery for AI Engineers
Stop using the GUI for engineering tasks. Learn to navigate the filesystem, manage environments with 'uv' and 'pip', and use shell pipes to process AI data logs like a PhD researcher.
- Docker for AI Deployment
Containerization ensures your AI app runs the same on your machine as it does in production. Learn to wrap your LLM services in Docker.
- SQL & Vector-Adjacent Data
Relational SQL for users, billing, and strict filters; optional pgvector in the same engine for semantic recall — plus the Prisma-shaped mental model used across this stack.
- Git & GitHub Workflows
Treat Git as the audit trail for code, prompts, and config: ignore secrets, branch for experiments, commit with intent, and wire PRs + CI the same way professional AI teams ship.
- Practical Graduation: CLI Expense Tracker
Consolidate everything: File I/O, JSON, Lists, and Functions. Build a real-world tool that you'll use as the template for every future AI data pipeline.