On Saturday, September 27th, I am launching a new course: Build Production-Ready Agentic-RAG Applications From Scratch! This is a fully hands-on course where we are going to deploy a production-ready Agentic-RAG application with LangGraph, FastAPI, and React! The first 30 people to sign up will get a 20% discount by applying the promo code FIRST20! So make sure to sign up early: From Prototype to Production: Ship Reliable and Scalable RAG Pipelines 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. What you’ll get out of this course - Orchestrate complex RAG pipelines with LangGraph and OpenAI API: Build a typed LangGraph that routes rewrite → retrieve → rerank → synthesize → cite → self-check with retries, timeouts, early-exit rules, and real tool calls, exposed as a clean HTTP API. - Build scalable asynchronous applications with FastAPI: Ship async FastAPI endpoints, well-typed request/response models, input validation, and sensible timeouts, ready to run locally and deploy to production. - Implement chatbot interfaces with React: Create a chat UI that shows citations and source previews, lets users scope queries, preserves safe chat history, and handles transient API errors gracefully. - Mitigate hallucinations with LLM judges, structured output, and context engineering: Cut errors via schema-aware chunking, dedupe and budgeted context packing, plus lightweight LLM checks and schema-constrained outputs to verify claims and enforce citations before responding. - Design effective LLM prompts for high-level control on generation output: Write prompts that steer behavior: system prompts, task decomposition, Pydantic/JSON-schema constraints, and clear rules for tone, citations, and safe refusals. - Develop end-to-end RAG applications using the software engineering best practices: Produce a maintainable codebase: clean module boundaries (ingest/retrieve/rerank/synthesize), typed configs, secrets/env management, reproducible local dev, and deploy that mirrors local.