intermediate track · 18 hours
Building Production AI Products
Take what you know and ship it. RAG, agents, evaluations, observability — the gap between a demo and a product.
Courses in this track
Course 1 · 6h
RAG from Scratch
Build a retrieval-augmented generation pipeline that actually retrieves the right thing — without LangChain magic boxes.
You'll be able to:
- Build an end-to-end RAG pipeline in TypeScript without a framework
- Pick the right embedding model for your data type and budget
- Diagnose why retrieval is failing using precision/recall metrics
Course 2 · 6h
AI Agents That Actually Ship
Cut through the agent hype. Build reliable, observable, debuggable multi-step systems.
You'll be able to:
- Choose between single-step LLM calls, ReAct agents, and tool-use loops based on the actual problem
- Build agents that fail gracefully and stop on infinite loops
- Add observability with tracing tools like Langfuse or LangSmith
Course 3 · 4h
Evals: How to Know Your AI Works
The discipline that separates 'demo magic' from 'production reliable'.
You'll be able to:
- Build an eval harness for any LLM feature in under an hour
- Use LLM-as-judge correctly — and know when not to
- Set up regression testing so a model upgrade can't silently break your product