AI Router and content system
Design an AI router that chooses between local models and frontier APIs with LiteLLM, privacy policies, quality scoring, shadow mode, observability, and human review for educational content.
What you will be able to do
- Design routing policies by privacy, cost, and difficulty
- Use LiteLLM as a gateway with keys, budgets, and fallbacks
- Measure quality with scoring, shadow mode, and traces
An AI Router prototype for generating and reviewing educational content with auditable routes.
Teams and creators combining local models, frontier APIs, and human review in real pipelines.
- • Basic Python
- • Understanding of LLM APIs and local models
- • Some LiteLLM or LLM observability context helps
Syllabus
module 01 / Router architecture
3 lessonsmodule 02 / Quality and production
3 lessonsComplete Aulafy map
How all courses connect
This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.
- 1Understand
- 2Apply or build
- 3Operate with confidence
Choose an application
Turn the foundation into a visible outcome: a website, a business improvement, media, or an interactive experience.
Continue into the technical branch when you need to maintain code, data, or infrastructure.
Build with code
Prepare your environment, work with coding agents, and run models while keeping control of your projects.
This branch prepares you to design and operate reliable AI systems.
Take systems to production
Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.
You do not need every course: choose the component your system needs and return as it grows.