Damien Benveniste

Cohort 1

Cohort 2

This is a complete guide on the original Transformer architecture and attention mechanisms! This includes 40 pages of every detail you need to know to start your journey in the world of Large Language Models.

The Attention Is All You Need: The Full Guide

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Building retrieval-augmented generation and search systems — vector databases, embeddings, ranking, and retrieval pipelines.

End-to-end: orchestrate and deploy agentic Retrieval-Augmented Generation with LangGraph, FastAPI, and React frontend in 2 weeks.

Build Production-Ready Agentic-RAG Applications From Scratch

Senior Principal Data Scientist

Principal

Vice President - Surveillance Analytics

Strategic AI/RAG Consultant

Data Scientist and ML/AI Trainer

Director

Data Scientist

Software Engineer

Software R&D

Technology Consultant 

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.✅ &nbsp;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 ✨&nbsp;A running Agentic RAG app (LangGraph + FastAPI + React) in a fork-and-ship monorepo.✨&nbsp;An ingestion/indexing pipeline with metadata, hybrid retrieval, and optional re-ranking.✨&nbsp;A chat UI with citations, source previews, and conversation memory that behaves.✨&nbsp;Deploy scripts and env templates to go live right after class.✨&nbsp;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.

From Prototype to Production: Ship Reliable and Scalable RAG Pipelines

Course syllabus

The backend and LangGraph nodes are Python; basics help you focus on RAG patterns instead of syntax or environment setup.

Python 3.10+ fundamentals (functions, modules, async)

Used for the minimal web UI and examples; JS fluency helps you send requests, handle streaming responses, and add simple interactions.

JavaScript basics (ES6 syntax and async)

If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students

Saturdays & Sundays

Hands-on project with community support for maximum learning

Course project

Course schedule

ML/LLM engineers ready to ship Agentic RAG: LangGraph + FastAPI + React, GCP deploy, citations, history-safe chat.

Full-stack/platform engineers adding RAG fast: opinionated stack, streaming FastAPI, React UI with citations, GCP deploy.

Founder/solutions architect proving value: grounded chat over your repos/docs, agentic flows, deploy to production.

Who is this course for

What you’ll get out of this course

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.

Orchestrate complex RAG pipelines with LangGraph and OpenAI API

Ship async FastAPI endpoints, well-typed request/response models, input validation, and sensible timeouts—ready to run locally and deploy to production.

Build scalable asynchronous applications with FastAPI

Create a chat UI that shows citations and source previews, lets users scope queries, preserves safe chat history, and handles transient API errors gracefully.

Implement chatbot interfaces with React

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.

Mitigate hallucinations with LLM judges, structured output, and context engineering

Write prompts that steer behavior: system prompts, task decomposition, Pydantic/JSON-schema constraints, and clear rules for tone, citations, and safe refusals.

Design effective LLM prompts for high-level control on generation output

Produce a maintainable codebase: clean module boundaries (ingest/retrieve/rerank/synthesize), typed configs, secrets/env management, reproducible local dev, and deploy that mirrors local.

Develop end-to-end RAG applications using the software engineering best practices

Rackspace Technology

Medallia

Bluestem Brands

Dell Technologies

The Real-World LLM Engineering Roadblocks You Face Today 👋 Transitioning from General ML to LLM Specialization: You’ve built recommendation engines or classifier models, but moving into Transformer‑centric development feels like learning a whole new discipline—no clear roadmap exists.👋 Lack of LLM‑Specific Career Path: You see “LLM Engineer” roles popping up on LinkedIn, but your current CV only shows “Data Scientist” or “ML Engineer.” You need hands‑on projects and artifacts to credibly make the jump.👋 Career Stalled by “Academic” Skillset: You can recite Transformer papers, but when asked, “Have you shipped an LLM feature end‑to‑end?” you have no answer—and no portfolio to prove it!👋 Prototype Meltdown Under Production Load: You’ve fine‑tuned a small model locally, but when you switch from 1 to 100 concurrent requests, your GPU memory spikes and inference grinds to a halt, because you’ve never applied continuous batching, KV caching, or paged‑attention in a live setting.👋 RAG Integration Headaches: Turning a standalone model into a live, Retriever‑Augmented Generation service becomes a multi‑week integration nightmare. How this course will help you Because we’ve packaged every stage of the LLM lifecycle, from career transition to production rollout, into a six‑week bootcamp that:✅ Guides Your Career Pivot: You’ll emerge with six polished GitHub projects, a deployment playbook, and RAG demos that transform your resume from “ML generalist” to “LLM Specialist.”✅ Attacks Each Pain‑Point Head‑On: Attacks each pain point head‑on with six job‑mirroring projects (from scratch → RLHF → scaling → deployment → RAG), so you never waste time on dead‑end tutorials✅ Live Code‑Along Workshops &amp; Office Hours: Tackle your own fine‑tuning bugs, scaling hiccups, and deployment errors alongside Damien in dedicated sessions, so you get hands‑on fixes for the exact issues you’ll face on the job.✅ Ready‑to‑Use Repos &amp; Playbooks: Grab our curated starter code, development scripts, deployment templates, and debugging checklists, so you can plug them straight into your next project without reinventing the wheel.✅ A Portfolio of Six Production‑Grade Projects: Leave with six end‑to‑end deliverables, from a Transformer built from scratch to a live RAG API, ready to showcase on GitHub, in performance reviews, or to hiring managers. No more scattered blog-hopping or generic bootcamps, this is the only cohort where you’ll master Transformer internals and ship production‑grade LLM systems while making the career leap you’ve been aiming for. What You’ll Actually Build and Ship Across six hands‑on projects, you’ll deliver deployable LLM components and applications, no fluff, just job‑ready code:✅ A Modern Transformer Architecture from scratch: Implement a sliding‑window multihead attention to slash O(N²) to O(N·w), RoPE for relative positional encoding, and the Mixture-of-Expert architecture for improved performance, all in PyTorch.✅ Instruction‑Tuned LLM: Fine‑tune a model with supervised learning, RLHF, DPO, and ORPO for instruction following on a real benchmark and compare performance gains.✅ Scalable Training Pipeline: Containerize a multi‑GPU job with DeepSpeed ZeRO on SageMaker to maximize throughput and minimize cost.✅ Extended‑Context Model: Modify RoPE scaling, apply 4/8‑bit quantization, and inject LoRA adapters to double your context window.✅ Multi‑Mode Deployment: Stand up a Hugging Face endpoint, a vLLM streaming API, and an OpenAI‑compatible server, all Dockerized and optimized for low latency.✅ End‑to‑End RAG Chat App: Build a FastAPI backend with conversational memory and a Streamlit UI for live Retrieval‑Augmented Generation. By the end of Week 6, you won’t just know these techniques, you’ll have shipped six production‑grade artifacts, each reflecting the exact pipelines, optimizations, and deployment routines you’ll use on the job. Live &amp; Recorded Content: Reinforce, Deepen, Accelerate ✨ 12 Interactive Live Workshops (3 hrs each): Each session follows the Concept&nbsp;→&nbsp;Code flow. I’ll introduce the day’s core topic (e.g. self-attention, LoRA, vLLM optimizations, ...), and we’ll implement the features step‑by‑step in code so you see exactly how theory maps to code. Bring your questions!✨ 10 + Hours of On‑Demand Deep‑Dive Lectures: Short videos (10–20 min) on Transformer internals, fine-tuning tricks, deployment optimizations. Watch before each project to hit the ground running. Step through every line of code at your own pace; perfect for review or catching up if you miss a live session. Downloadable slide decks, annotated notebooks, and cheat sheets you’ll reference long after graduation. Why This Matters: Live workshops turn recorded concepts into actionable skills. You’ll see how theory maps directly onto code, get instant feedback, and internalize best practices. Then, recorded lectures become your asynchronous safety net, letting you revisit tricky topics, prepare for upcoming labs, and solidify your understanding on demand.

From Prototype to Production: Ship Scalable LLM Systems in 6 Weeks

Live sessions - every session will be recorded and available after

Hands-on projects with community support for maximum learning

Weekly projects

Senior ML Engineers upgrading to LLMs, frustrated by prototypes that crumble under real‑world load and runaway inference costs

Data Scientists moving into LLM workflows, stalled by opaque fine-tuning pipelines and multi‑week integration headaches.

Recent CS Graduates specializing in LLMs, eager to move beyond theory but lacking hands‑on experience

<ul><li>Implement modern Transformer architecture from Scratch</li><li>Fine-tune LLMs for instruction following</li><li>Build a scalable training pipeline</li><li>Fine-tune LLM to extend context window with RoPE and QLoRA</li><li>Deploy API endpoints for production-ready LLM</li><li>Build end‑to‑end RAG chat app</li></ul>

Deliver Six Production‑Grade LLM Artifacts

Spend dedicated lab sessions wrestling with production‑scale puzzles, OOMs, tail‑latency spikes, and integration snags, until you persevere and ship battle‑tested solutions.

Tackle Real‑World LLM Challenges

12 interactive sessions where you’ll implement each module live, ask questions, troubleshoot your project code, and internalize battle‑tested best practices.

Master Core Concepts in Live Code‑Along Workshops

10+&nbsp;hours of short videos covering Transformer math, ZeRO strategies, vLLM optimizations, and more. Complete with annotated notebooks and slides.

Reinforce with On‑Demand Deep‑Dive Lectures

GitHub‑style feedback rounds on your project submissions

Peer & Instructor Code Reviews

Revisit recordings anytime, join a Slack of LLM engineers, share new tactics, and continue growing long after the cohort wraps.

Lifetime Access & Private Community

A 6‑week, live bootcamp for ML engineers to architect, fine‑tune, and deploy scalable LLM applications through six real‑world projects

Learn directly from Damien Benveniste

Damien Benveniste

Courses

Build Production-Ready Agentic-RAG Applications From Scratch

Build Production-Ready LLMs From Scratch

Lightning Lessons

Build The Self-Attention in PyTorch From Scratch

Resources

The Attention Is All You Need: The Full Guide

The Attention Is All You Need: The Full Guide