Welcome to Module 10 — production & scaling
Before we begin
Modules 1–9 taught you how AI works — from math through RAG, agents, and multimodal models. Module 10 teaches how to ship — the layer most tutorials skip and interviews expect.
Interview focus: caching, token cost, latency bottlenecks, rate limits, monitoring, graceful errors — plus the course capstone.
Figure
Module 10 at a glance
What Module 10 covers
| Topic | What you will understand |
|---|---|
| Model serving | API routes, streaming, hosted vs self-hosted |
| Caching | Redis, cache keys, TTLs, invalidation |
| Rate limiting | Protect budget and upstream quotas |
| Cost | Token math, trimming context, model routing |
| Monitoring | Logs, metrics, traces, user-visible errors |
| Capstone | Production GenAI app combining RAG + agents |
Why this module matters
A demo on localhost is not a product. Production GenAI apps fail on:
- Runaway spend — agent loops + long RAG context.
- Slow UX — users wait for full completion with no streaming.
- Silent outages — LLM timeouts with no logs.
- Abuse — one script draining your API key.
This module closes those gaps and delivers your Week 10 capstone.
Before you start
Required: Module 8 travel planner and Module 9 multimodal overview (or equivalent comfort with RAG + agents).
For lessons and project:
- Redis locally (
docker run -p 6379:6379 redis) or Upstash free tier - Existing Module 7 RAG index and Module 8 agent code to extend
- Optional: Vercel/hosting account for deployment section