Advanced Python for AI Engineers

A practical, interview-ready, production-oriented Advanced/Modern Python learning system — built specifically for AI Engineers.

This is not a generic Python course. Every topic exists to answer one question:

Why does an AI Engineer need this Python concept, how does it work internally, how do I use it in an AI system, and when should I NOT use it?

🚧 Status: under active construction. The full folder structure and curriculum outline are in place; module content is being written incrementally. Each module's README.md shows its own status (🚧 Planned or ✅ Written) — see Progress below.

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Who this is for

Engineers who already know basic Python and are building (or want to build) LLM apps, RAG systems, AI agents, LangGraph-style workflows, FastAPI AI backends, or production AI services — and want their Python to hold up under real concurrency, real failure modes, and real interviews.

What this teaches

• Modern Python core: iterators, generators, async/await, context managers, decorators
• Production Python: type hints, dataclasses, Pydantic, testing, packaging, config
• AI-system Python: asyncio concurrency, async HTTP, streaming (SSE/WebSockets), retries, caching, background tasks
• Deep Python: memory model, the GIL, threads vs. processes, contextvars, magic methods
• Reusable patterns for LLM/RAG/agent architectures, and the interview questions that go with all of the above

What this intentionally does NOT teach

• Basic Python syntax from scratch (variables, loops, if/else)
• General web development unrelated to AI backends
• A specific LLM provider's SDK in depth
• LangGraph/LangChain internals themselves — Module 28 and Project 06 teach the Python patterns those frameworks are built on, not the frameworks

Skill map

Python
 ↓
Modern Python        (iterators, generators, decorators, context managers)
 ↓
Async Python         (asyncio, async generators, streaming)
 ↓
Production Python     (typing, Pydantic, testing, packaging, config)
 ↓
AI Engineering Python (concurrency, HTTPX, retries, caching, background jobs)
 ↓
LLM / RAG / Agents
 ↓
Production AI Systems

Learning order

The repo is organized into four levels, roughly in build order:

Level	Focus	Modules
Foundation	Quick review before diving in	00
Level 1 — Modern Python Core	iterators, generators, async/await, context managers, decorators	01–06
Level 2 — Production Python	type hints, dataclasses, Pydantic, protocols, serialization, testing, config, DI, packaging	07–09, 11, 18, 19, 21–23
Level 3 — AI-System Python	concurrency, HTTPX, streaming, retries, caching, queues, logging, production/AI patterns	12–17, 20, 27, 28
Level 4 — Deep Python	advanced OOP/magic methods, performance & memory, GIL/processes/threads, contextvars	10, 24–26

Work top to bottom through the numbered folders (00-... → 28-...); each module lists prerequisites in its own README.md. code-reading/ and debugging/ can be done alongside any level. projects/ are meant to be attempted after their listed prerequisite modules.

Repository structure

advanced-python-for-ai-engineers/
├── README.md, AGENTS.md, CLAUDE.md   ← you are here / agent & content rules
├── 00-python-foundation-review/  … 28-ai-engineering-patterns/   ← the curriculum
├── code-reading/                  predict-the-output exercises
├── debugging/                     intentionally broken code + fixes
└── projects/                      6 mini projects combining modules

See AGENTS.md for the full structure, the mandatory topic template, and the content style rules every module follows once written.

Progress

Status	Meaning
🚧 Planned	Folder + scope exist; full lesson not yet written
✅ Written	Follows the full topic template with runnable code

Module	Status
01-functions	✅ Written
02-iterators-generators	✅ Written
03-asyncio	✅ Written
04-async-generators-streaming	✅ Written
05-context-managers	✅ Written
06-decorators	✅ Written
07-type-hints	✅ Written
08-dataclasses	✅ Written
09-pydantic	✅ Written
10-advanced-oop	✅ Written
11-protocols-generics	✅ Written
12-concurrency	✅ Written
13-httpx-async-http	✅ Written
14-streaming-sse-websockets	✅ Written
15-error-handling-retries	✅ Written
16-caching	✅ Written
17-queues-background-tasks	✅ Written
18-serialization	✅ Written
19-testing-pytest	✅ Written
20-logging-observability	✅ Written
21-config-environments	✅ Written
22-dependency-injection	✅ Written
23-packaging-modern-python	✅ Written
24-performance-memory	✅ Written
25-gil-processes-threads	✅ Written
26-contextvars	✅ Written
27-production-python-patterns	✅ Written
28-ai-engineering-patterns	✅ Written
everything else	🚧 Planned

Check each folder's README.md for its current status and planned subtopics.

Setup

• Python version: 3.12+
• No repo-wide dependencies yet — each module/project is self-contained and will list its own requirements as it's written (a small requirements.txt or PEP 723 script header inside that folder).

git clone https://github.com/himanshu231204/advanced-python-for-ai-engineers.git
cd advanced-python-for-ai-engineers
python3 --version   # confirm 3.12+

How to run examples

Once a module contains runnable code, run it directly:

python3 01-functions/example.py

If a project folder has its own pyproject.toml (added as projects are built), use uv run from inside that folder instead — see that project's README.md.

How to run tests

Testing conventions live in 19-testing-pytest/. Most module examples are self-checking (they print expected output) rather than backed by a test suite; project 05 has a real pytest suite — see projects/05-production-ai-service.

Roadmap

See ROADMAP.md for a phased, paced path through the curriculum (with checkpoints and an alternative order for readers who already know foundational Python), and AGENTS.md for how content gets added.

Reference docs

Cross-curriculum references that sit alongside the numbered modules:

• ROADMAP.md — phased learning path with checkpoints
• CHEATSHEET.md — every module's cheat sheet in one scannable page
• INTERVIEW.md — every module's interview questions in one bank
• PATTERNS.md — reusable patterns gathered by theme, across modules and projects
• GLOSSARY.md — alphabetical glossary of terms used throughout the curriculum
• PYTHON_TO_AI_ENGINEERING.md — organized by AI-engineering task instead of by module

Contributing / extending

This repo is built module-by-module. If you're adding content (human or AI agent), read AGENTS.md first — it defines the exact template, style, and structure every topic must follow so the repo stays consistent as it grows.