🐍 Python for AI Engineering

A structured 2-week Python learning sprint focused exclusively on the skills needed to build production-grade AI applications — LLM pipelines, agentic systems, and AI APIs.

This is not a general Python course. Every topic here has a direct line to real-world AI engineering with LangChain, LangGraph, LangSmith, and the broader LLM ecosystem.

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👤 About This Repository

This repo documents a personal learning journey from Engineering Manager → AI Engineer, built by someone with 15+ years of software development experience who wanted to close the gap between general Python knowledge and AI-specific engineering patterns.

Progress is committed daily, one folder per day, with working code examples and exercises.

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🎯 Who Can Benefit From This

This repo is for you if:

• You're an experienced software engineer (any language) transitioning into AI engineering
• You're a Python developer who wants to level up specifically for LLM application development
• You're a tech lead or engineering manager moving hands-on into the AI space
• You understand programming fundamentals and want to skip the basics and go straight to patterns that matter in production AI systems

This is not for absolute beginners. If you're new to programming, start with a general Python course first.

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🗺️ The 2-Week Curriculum

Week 1 — Core Python for AI Systems

Day	Topic	Key Concepts
Day 01	Python Environment & Tooling	pyenv, Poetry, pyproject.toml, VS Code setup, Ruff, pre-commit
Day 02	Type System & Pydantic v2	Type hints, TypedDict, BaseModel, validators, serialization, LLM I/O schemas
Day 03	Async Python	asyncio, async/await, gather, queues, streaming responses, httpx
Day 04	Generators & Iterators	yield, async generators, itertools, lazy evaluation, token streaming
Day 05	Decorators & Context Managers	Stacking decorators, functools, contextlib, retry wrappers, tracing
Weekend	Review & Reinforce	Revisit async project, read LangChain source, Pydantic challenges

Week 2 — Advanced Patterns for AI Application Dev

Day	Topic	Key Concepts
Day 08	OOP Patterns in AI Frameworks	ABCs, Protocols, Mixins, Factory pattern, LangChain class hierarchy
Day 09	Concurrency & Performance	GIL, ThreadPoolExecutor, parallel LLM calls, semaphores, profiling
Day 10	Data Handling & Parsing	JSON encoders, LLM output parsing, regex extraction, orjson, YAML
Day 11	Testing & Observability	pytest, mocking LLM calls, structlog, OpenTelemetry, snapshot testing
Day 12	Production FastAPI	Async endpoints, WebSocket streaming, dependency injection, Docker
Weekend	Capstone Project	Full LLM pipeline app — async + streaming + Pydantic + FastAPI + tests

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🏗️ Repository Structure

python-ai-engineering/
│
├── week1/
│   ├── day01_tooling/
│   │   └── notes.md
│   ├── day02_types_pydantic/
│   │   ├── type_system.py
│   │   ├── pydantic_basics.py
│   │   └── llm_schemas.py
│   ├── day03_async/
│   ├── day04_generators/
│   └── day05_decorators/
│
├── week2/
│   ├── day08_oop_patterns/
│   ├── day09_concurrency/
│   ├── day10_data_parsing/
│   ├── day11_testing/
│   └── day12_fastapi/
│
├── capstone/
│   └── llm_pipeline_app/
│
├── pyproject.toml
├── poetry.lock
├── .env.example
├── .gitignore
└── README.md

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🛠️ Tech Stack

Category	Tools
Runtime	Python 3.11+, pyenv
Dependency Management	Poetry, pyproject.toml
Type Safety	Pydantic v2, typing, mypy
Async & HTTP	asyncio, httpx
API Framework	FastAPI
LLM SDKs	openai, anthropic
AI Frameworks	LangChain, LangGraph (coming next)
Testing	pytest, pytest-asyncio, pytest-mock
Observability	structlog, OpenTelemetry
Linting & Formatting	Ruff, Black
Environment	VS Code, Jupyter Notebooks, Docker

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🚀 Getting Started

Prerequisites

• macOS / Linux / Windows (WSL2)
• Python 3.11+ via pyenv
• Poetry for dependency management
• VS Code with Python + Pylance + Ruff extensions

Setup

# Clone the repo
git clone https://github.com/yourusername/python-ai-engineering.git
cd python-ai-engineering

# Install dependencies
poetry install

# Copy environment variables
cp .env.example .env
# Add your API keys to .env

# Activate the environment
poetry shell

Environment Variables

# .env.example
OPENAI_API_KEY=your_key_here
ANTHROPIC_API_KEY=your_key_here

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📈 Learning Outcomes

By the end of this sprint, you will be able to:

• ✅ Write fully typed, async Python with confidence
• ✅ Design Pydantic schemas for LLM inputs and outputs
• ✅ Handle streaming token output from any LLM provider
• ✅ Build retry and rate-limit wrappers for LLM APIs
• ✅ Run parallel LLM calls without exceeding rate limits
• ✅ Parse and validate unstructured LLM responses robustly
• ✅ Write tests for AI code including mocking LLM calls
• ✅ Expose AI logic via a production-ready FastAPI server
• ✅ Read and understand LangChain and LangGraph source code
• ✅ Architect a Python AI application from scratch

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🔭 What Comes Next

This repo is Phase 1 of a longer AI engineering journey:

Phase	Focus
Phase 1 (this repo)	Python foundations for AI engineering
Phase 2	LangChain — chains, prompts, retrievers, memory
Phase 3	LangGraph — stateful agents, multi-agent systems
Phase 4	LangSmith — tracing, evaluation, observability
Phase 5	Production deployment — RAG systems, agent APIs

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📝 Commit Convention

Daily progress follows this commit style:

day02: add Pydantic v2 fundamentals and LLM I/O schemas
day03: implement async streaming client with httpx
capstone: complete LLM pipeline with FastAPI streaming endpoint

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📄 License

MIT — use anything here freely for your own learning.

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_{Built with focus and coffee · AI Engineering Sprint · 2025}