Skip to content

Prerequisites & Setup

What you need

Skills

  • Python 3.11+ at intermediate level (functions, classes, async basics)
  • Comfort with HTTP APIs and JSON
  • Git basics; a GitHub account
  • Optional: Docker, basic Linux CLI

Hardware

Path Minimum Comfortable
API-only (cloud models) 8 GB RAM laptop 16 GB
Local 1–3B Q4 (classify / extract) 8 GB RAM 16 GB
Local 7B–8B Q4 16 GB RAM 32 GB + GPU (8 GB+ VRAM)
Fine-tuning / large RAG 16 GB + GPU 24 GB+ VRAM

Fit weights + KV cache + OS headroom — details in Module 17 §7. A 7B that swaps is worse than a 3B that stays resident.

Accounts (pick what you need)

  • At least one LLM provider: OpenAI, Anthropic, Google AI, or free local via Ollama
  • Optional: Hugging Face, Pinecone/Weaviate/Qdrant, cloud host (Fly, Railway, AWS, Azure, GCP)

Model IDs in this course are placeholders

Strings like gpt-4o-mini, claude-sonnet-xxxxxx, or llama3.2 show role (cheap classifier vs stronger generator vs local SLM), not a pin you must use in 2026. The xxxxxx suffix marks a fill-in — it is never a real ID. Check your provider’s current catalog before you copy a snippet.

Environment setup

1. Clone and Python env

Python: 3.11–3.13 recommended (^3.11,<3.14 in pyproject.toml). Core teaching modules are stdlib-only.

git clone https://github.com/<you>/AIEngineering.git
cd AIEngineering

# Poetry (repo default)
poetry config virtualenvs.in-project true
poetry env use python3.11   # if multiple Pythons installed
poetry install --with dev
# Optional stock/data track extras (pandas, numpy, sklearn, …):
# poetry install -E track-data

# Or venv + pip (core tests only)
python3.11 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -U pip pytest
PYTHONPATH=. pytest tests/ -m "not track_data"

2. Core learning packages

Install as you need modules (not everything on day 1):

pip install openai anthropic python-dotenv httpx tenacity tiktoken
pip install pydantic pydantic-settings
# RAG
pip install numpy scikit-learn  # baselines / embeddings helpers
# Optional later
# pip install chromadb sentence-transformers fastapi uvicorn
# pip install peft transformers accelerate bitsandbytes  # fine-tune

3. Secrets

Create .env (never commit it):

# .env
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
GOOGLE_API_KEY=
# Optional
HF_TOKEN=

Load with python-dotenv or your shell.

4. Smoke test (provider-agnostic idea)

"""setup_smoke.py — adapt to your provider."""
import os
from dotenv import load_dotenv

load_dotenv()

def main():
    # Prefer Anthropic or OpenAI if keys exist; else print local guidance
    if os.getenv("OPENAI_API_KEY"):
        from openai import OpenAI
        client = OpenAI()
        r = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": "Reply with exactly: ok"}],
            max_tokens=16,
        )
        print(r.choices[0].message.content)
        return
    if os.getenv("ANTHROPIC_API_KEY"):
        import anthropic
        client = anthropic.Anthropic()
        r = client.messages.create(
            model="claude-sonnet-xxxxxx",
            max_tokens=16,
            messages=[{"role": "user", "content": "Reply with exactly: ok"}],
        )
        print(r.content[0].text)
        return
    print("No API key found. Install Ollama and run: ollama run llama3.2")

if __name__ == "__main__":
    main()

# macOS / Linux / Windows
# https://ollama.com
ollama pull llama3.2
ollama run llama3.2

Course sandbox package

This repo includes runnable teaching code under src/:

Module Purpose
src.security Sanitization + PII redaction
src.prompts Template render helpers
src.context_memory Session memory + token budget
src.rag TinyRAG + RRF
src.evals Golden-set runner
src.cost Router, cache, spend ledger
src.agents Single-agent loop
src.audit Hashing audit events 
poetry install
poetry run pytest tests/ -v

Guided exercises: Exercises · grading: Assessment.

Capstone starter (no Poetry, no API keys)

The production-AI skeleton lives next to docs/, not inside src/:

cd capstone-starter
pip install -r requirements.txt
uvicorn app:app --reload
pytest tests/test_api.py tests/test_eval.py -v

Walk the five gates. Track day-1 slices: tracks/starters/.

your-work/
  prompts/
  notebooks/          # exploration only
  src/                # or extend this repo's src/
  tests/
  evals/              # golden sets, prompt fixtures
  .env
  PROGRESS.md         # daily log

Windows notes

  • WSL2 is strongly recommended for path parity with tutorials: Install WSL
  • Open the WSL folder in VS Code with the Remote – WSL extension
  • Keep Git line endings consistent (core.autocrlf)

macOS / Linux: native terminal is fine; VS Code or Cursor optional.

Docs site (contributors)

pip install -r requirements-docs.txt
mkdocs serve   # http://127.0.0.1:8000
mkdocs build   # site/ for static export

Next