The Real-World AI Engineering Roadblocks You Face Today

👋 Prototype → Production Gap — Moving from a notebook demo to a secure, observable, multi-tenant service requires orchestration, evals, guardrails, and ops most teams lack.

👋 “Easy RAG” vs “Reliable RAG” — Anyone can retrieve-then-generate; making answers faithful, fresh, fast, and cost-controlled under real traffic is the hard part.

👋 Framework Overload — The ecosystem is noisy; you need clear criteria (maturity, extensibility, latency, cost) and reference patterns to choose confidently.

👋 It’s Software Engineering First — Success hinges on clean interfaces, tests, typed configs, tracing, CI/CD, and change management—not just prompts and models.

👋 From Laptop to 1M Users — Scaling demands streaming, batching, caching, autoscaling, and SLOs, or your p95 explodes and costs spiral.

How this course will help you

✅ Ship a real Agentic RAG app, not a demo — Stand up an end-to-end stack—LangGraph → FastAPI → React, that runs locally today and deploys via a clean, fork-and-ship monorepo.

✅ Make retrieval dependable, not lucky — Adopt schema-aware chunking, strong dense embeddings with sensible metadata filters, and context packing with citations so answers stay faithful, fresh, and concise.

✅  Harden agentic workflows — Design a typed LangGraph state and build nodes for rewrite → retrieve → rerank → synthesize → cite → safety-check, with retries and timeouts so plans don’t loop or stall.

✅ Scale the experience, not the headaches — Enable server-streaming in FastAPI, cap top-k, trim context budgets, and add early-exit rules; deploy with autoscaling so you can serve real traffic without infra fuss.

✅ See enough to fix things fast — Bake in structured logs (no vendor tracing), per-step timing counters, and UI breadcrumbs/citations to follow query → context → answer and spot common failure patterns quickly.

✅ Choose frameworks with confidence — Follow an opinionated reference architecture plus a simple choice rubric (maturity, extensibility, latency, cost, swap effort) so you know when to stick—and how to swap components without rewrites.

✅ Write maintainable RAG code — Use clean module boundaries (ingest / retrieve / rerank / synthesize), typed configs (Pydantic Settings), and sensible secrets/env management so your team can extend it safely.

You’ll walk away with

✨ A running Agentic RAG app (LangGraph + FastAPI + React) in a fork-and-ship monorepo.

✨ An ingestion/indexing pipeline with metadata, hybrid retrieval, and optional re-ranking.

✨ A chat UI with citations, source previews, and conversation memory that behaves.

✨ Deploy scripts and env templates to go live right after class.

✨ A framework choice memo + adapters to swap models/vector stores without starting over.

Bottom line: this isn’t a vitamin, it’s a blueprint you can put in production.