Hands-on Track — AI Infrastructure for Real Business Problems
Last updated 10/17/2026
Course › Hands-on track

Hands-on Track — AI Infrastructure for Real Business Problems

實作專題：解決真實商業問題的AI基礎架構 · 10 labs, one store, runnable code · Download the lab code (zip, 1.0 MB)

A 10-lab, code-first track that complements the 12 modules of the main course. Every lab solves one problem for the same fictional store, Kettle & Kiln (an online tea-ware shop in Oakland), using one shared dataset. Each lab ships runnable Python 3.12 / FastAPI / Docker Compose code with tests.

How the Track Works — 課程運作方式

data/generate_data.py regenerates the CSVs deterministically (seed 20261015; "today" in the data is 2026-10-15). - One client for every model. labs/common (llmkit) talks to Ollama, llama.cpp, LM Studio, OpenAI or any OpenAI-compatible URL. Its offline mock provider makes every test suite run without a GPU or an API key. - Each lesson has the same sections: - business problem - architecture (diagram) - prerequisites - step-by-step setup with exact commands - how to test - what was run on the build box - cost & performance notes - common failures - stretch exercise - deliverable checklist - Honesty rules. Results quoted in the lessons are single observed runs on a CPU-only build box with small models (qwen2.5:0.5b/1.5b, nomic-embed-text). They are not benchmarks. No prices are quoted; you fill in labs/09/pricing.yaml from your provider's page. Anything not run on the build box is marked ⚠️ Not run.

Labs — 實作單元

# Lab Business problem Maps to module
01 Model Access: Local vs Cloud Where may each request go? One client for all backends; PII stays local M1, M10
02 Prompts as Code + Evals A prompt edit silently broke ticket routing M2
03 Product-Listing Generator Service Bilingual listings without invented specs; Shopify CSV M3
04 Support Knowledge Base (RAG) Answer only from policies, with citations, or refuse M5
05 Customer-Service Agent Refunds by policy, identity checks, humans for safety M6, M8
06 n8n Automations Reviews, tickets, leads triaged and routed automatically M6
07 Sales-Data Copilot Plain-language analytics that can't modify data or invent numbers M4
08 Self-Hosting 24/7 on a NAS/Mac: Compose, Caddy, Cloudflare Tunnel, GPU, TrueNAS M10
09 AI Gateway & Observability Spend per feature, PII redaction, budgets, traces M12
10 Capstone Platform Everything behind one gateway; smoke, load, production checklist M11, Capstone

Getting Started — 開始使用

cd hands-on/labs/01
uv venv --python 3.12 && source .venv/bin/activate
uv pip install -e ../common -r requirements.txt
LLM_PROVIDER=mock pytest -q            # works offline
cp .env.example .env && python doctor.py

Test Status on the Build Box — 測試狀態

Lab Offline tests (mock) Live run on build box (Ollama, CPU)
01 8 passed, 1 skipped (live-only) doctor / chat / compare / live test ✅
02 5 passed eval reports saved (labs/02/reports/)
03 8 passed 4/4 drafts, 0 hard-rule failures, all needs_review
04 8 passed (incl. pgvector) hit@k 100%, cite_ok 87%, keywords 73%, grounded 100% (n=15)
05 15 passed 4 scenarios; discount-code incident → guard added
06 9 passed real n8n 2.43.7: 39/39 webhooks 200 (mock model); live-model replay not completed
07 14 passed golden 0/8 on 1.5B (3 wrong-but-verified)
08 validate.sh all OK (config only) ⚠️ no Docker daemon: stack not started
09 9 passed redaction + restore via Ollama, report ✅
10 2 passed (full no-Docker smoke) smoke 4/5 e2e on 1.5B (copilot failed)

Not run anywhere on the build box: Docker builds / compose up, GPUs, cloud APIs, TrueNAS, a live Cloudflare Tunnel/Access, a Langfuse server, Slack, Open WebUI. See each lesson's "What was run" section.

Folder Layout — 目錄結構

hands-on/
  README.md            this page
  NN-<slug>.md         lessons 01–10
  PROGRESS.md          build log
  data/                shared dataset (CSV + docs + eval)
  labs/common/         llmkit shared client + mock
  labs/01 … labs/10/   code, tests, Dockerfiles, compose, .env.example, README per lab
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