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Flagship certification · Self-paced

Become a Full Stack AI Engineer

And position yourself for the roles, projects, and opportunities that AI is creating right now.

Try 6 lessons free
Louis-François Bouchard Taught by Louis-François Bouchard and the Towards AI teamCo-founder & CTO · co-author, Building LLMs for Production
92lessons across the full AI stack
60+ hrsof code-heavy, project-based learning
1,000+AI engineers already enrolled
01Trusted voices

Industry leaders on the course

“The most comprehensive textbook to date on building LLM applications… prompting, RAG, agents, fine-tuning, and deployment, all essential topics in an AI Engineer's toolkit.”

Jerry Liu
Jerry Liu
Co-founder and CEO, LlamaIndex

“A truly wonderful resource that develops understanding of LLMs from the ground up, from theory to code and modern frameworks. Highly recommend.”

Pete Huang
Pete Huang
Co-founder, The Neuron

“An incredible survey of all the real-world problems one encounters when trying to productionize an LLM, as well as multiple solutions to each roadblock.”

Nick Singh
Nick Singh
Founder, DataLemur

“A comprehensive and well-rounded resource that covers all the fundamentals of LLMs with a well-struck balance between theory and code.”

Tina Huang
Tina Huang
Founder, Lonely Octopus · ex-Meta

“It covers the foundational aspects of LLMs as well as advanced use-cases like fine-tuning, Retrieval Augmented Generation and Agents. Valuable to anyone looking to dive into the field quickly and efficiently.”

Jeremy Pinto
Jeremy Pinto
Senior Applied Research Scientist, Mila

“If you are interested in AI Engineering, this course is one of the few within the AI space worth your time and money.”

Paul Iusztin
Paul Iusztin
Author, LLM Engineer's Handbook
02Why this course exists

The gaps between developer and AI engineer

We close them with real projects and the exact skills companies are hiring for.

Software engineering is the easy half

Learn to engineer around probabilistic systems

Design reliable systems on top of unpredictable models, using prompt engineering, context engineering, and RAG to control what the model knows, and fine-tuning to control how it behaves.

The talent gap is your opportunity

Get trained in exactly what the role demands

The AI engineering role is only 2 to 3 years old; the window to get in early is still open. Master the full stack, from choosing the right models and architectures to evaluating, optimizing, and deploying at scale.

Demos are easy. Production is the test.

Ship real, deployable LLM products

Move beyond notebooks and build something that gives you a competitive edge in the job market, launches your startup idea, or drives a new product at your company.

Experience the course before you commit

Explore the first 6 lessons free. No card required.

Unlock free preview
03Hands-on from day one

What you'll build

A production-grade AI tutor and a complete LLM toolkit through hands-on capstone projects.

RAG AI Tutor

A fully deployed AI tutor, built from scratch across 60+ lessons.

  • Prompt engineeringsystem prompts and iterative evaluation
  • Data pipelinescrape, parse, and clean data with Firecrawl, LlamaParse, and web APIs
  • RAG pipelinebuilt from scratch, then scaled with LlamaIndex and vector databases
  • Advanced retrievalhybrid search, re-ranking, metadata filtering, query augmentation
  • Fine-tuningGPT-4o Mini and embedding models, optimized for your use case
  • Production deploymentFastAPI, Gradio UI, Hugging Face Spaces

Full LLM Toolkit

The broader skills the role demands, beyond the central project.

  • Agent architecturetool use, LLM pipelines, autonomous task execution
  • Framework fluencyLangChain, LlamaIndex, and OpenAI Assistants side by side
  • Multi-modal capabilitiesimage generation, speech, diffusion models
  • Observabilityproduction tracing with Langsmith, Phoenix, and Langfuse
  • LLM optimizationquantization, pruning, distillation, speculative decoding

Your Own LLM Product

Everything applied to an LLM application you choose and ship.

  • Product strategy175 project ideas, niche selection, defensibility frameworks
  • Business contextdeep dives on Perplexity, Harvey AI, and Consensus
  • Custom RAG applicationyour own idea, fully built and deployed on Hugging Face
  • Certified deliveryreviewed by our team; portfolio-ready for jobs, MVPs, or pitches
  • Incubation opportunitystandout projects get direct support to find work or launch

Build your first LLM product this week

A fully deployed LLM application / Portfolio-ready skills with certification / Fundamentals that outlast trends
Enroll now self-paced · regular updates · risk-free enrollment
04Student outcomes

Hear from the engineers who took the leap

Highly recommend this course

This course is highly valuable. You can learn a lot about different critical aspects of LLMs and RAG. Before taking it, I used to wander through endless resources, most of which were, in fact, low quality. One of the things I love about the course is that it's not just a series of video lectures; it includes many code notebooks with detailed explanations, allowing you to learn by coding and engaging, which is the best approach. In order to create a good generative AI app with LLMs, a person should be knowledgeable about effective prompting, choosing the right tools, data collection and curation, vector databases, query engines, building RAG systems, creating agents, evaluation methods, and deploying efficiently. This course covers ALL of them.

Farhad Dalirani

Excellent course

The course covers an immense amount of information. I feel like I really learned a ton about working with LLMs and how to develop and deploy my own applications.

Martin Ballard

Complete and practical

Super comprehensive with a good balance between theory and hands-on. It also is way more affordable than many other options out there. What makes the course stand out is that it doesn't stop at the basics; it actually goes into design, deployment and optimization. A great choice to master AI engineering.

Mikhail Rybalchenko

Truly practical from an engineering perspective

Building concepts from the ground up. From an engineering perspective, going through the code and running the examples helps to grasp a deep understanding. Very practical, and it equips you with the tooling to face real-world use cases.

Victor Palomares

Exceptional course with in-depth resources

This course stands out for its exceptional structure and quality of references that truly deepen the learning experience. The materials are carefully curated and provide a comprehensive foundation that encourages further exploration of the topics covered. Each reference not only supports the lessons but also enriches understanding, making complex concepts accessible and engaging. Highly recommended for anyone looking to expand their knowledge with quality resources.

Vitor Ramos

A must-have for LLM developers and data science students

I find the book and training videos a must-have for anyone interested in learning about LLMs and developing a career in them. I have recommended them to my students in Data Science and AI to accelerate their understanding and build MVPs.

Badshah Mukherjee
05Made for builders

Who is this course for?

Software developers, ML engineers, data scientists, and CS or AI students ready to move into an LLM developer role and start building.

Going all-in on AI

The developer making AI their primary skill set

Build a portfolio, earn certification, and walk into high-demand LLM and AI engineering roles with proof you can deliver.

Leading AI at their company

The engineer driving change from the inside

You see AI transforming your industry. Gain the hands-on experience to lead initiatives, deploy real solutions, and drive innovation from the inside.

Building a product

The developer with a product idea

You have an idea and the technical chops to build it. Turn it into a deployed, production-grade LLM application within 50+ hours of focused learning.

06The live syllabus

Course curriculum

Four parts, 92 lessons, one fully deployed product. The first six lessons are free to preview.

01Course Introduction and Understanding LLMs9 lessons
  1. 1From Beginner to Advanced LLM Developer | The Towards AI AcademyFree preview
  2. 2Course Introduction and LogisticsFree preview
  3. 3Syllabus In-DepthFree preview
  4. 4How to use LLMs via APIFree preview
  5. 5An LLM Developer's Role is to Address AI's Weaknesses and Increase its CapabilitiesFree preview
  6. 6Why Prompt Engineering, RAG, Tools, and Fine-tuning?Free preview
  7. 7Understanding AI and Large Language Models without Math or Code
  8. 8Alien Intelligence Leads to Unintuitive Mistakes
  9. 9Course Tools Guide
02Part 1: Building our RAG AI Tutor — Using LLMs11 lessons
  1. 1Section Overview: Building Our RAG AI Tutor; Introduction to Using LLMs
  2. 2To use a LLM or to not use it?
  3. 3Choosing your LLM Part 1: Open vs Closed Source and Key Comparison Metrics
  4. 4Choosing your LLM Part 2: Key Benchmarks and Leaderboards
  5. 5Choosing your LLM Part 3: The Leading Open and Closed LLMs Today
  6. 6What is Prompting? Talking with AI Models…
  7. 7Prompting 101
  8. 8What is Prompt Injection? Can you Hack a Prompt?
  9. 9Building our AI Tutor System Prompt. Introducing Prompt Injection and Hacking
  10. 10Evaluating and Iterating Prompts
  11. 11Quiz
03Part 1: RAG Basics7 lessons
  1. 1Section Overview: Using Basic RAG for Our Project
  2. 2What is RAG?
  3. 3RAG 101
  4. 4Building your Knowledge Base for RAG
  5. 5Building a basic RAG pipeline from scratch
  6. 6Build an OpenAI GPT with our AI blog data
  7. 7Quiz
04Part 1: RAG with LlamaIndex8 lessons
  1. 1Section Overview: Developing a RAG AI Tutor With LlamaIndex
  2. 2Basic RAG with LlamaIndex
  3. 3How vector DBs work and when to use one
  4. 4Using a Vector Database
  5. 5Improving Data Sources and Prompts
  6. 6RAG Evaluations
  7. 7Evaluating your RAG Pipeline
  8. 8Quiz
05Part 1: Using other LLMs and Embedding Models7 lessons
  1. 1Section Overview: Using Other LLMs and Embedding Models
  2. 2Security, Privacy & Cost; Comparing 5 key ways to access LLMs
  3. 3Using Llama 3.1 70B on Together.ai and comparing RAG performance
  4. 4Ollama Tutorial: Running Deepseek Distill Locally
  5. 5Multimodal LLMs and Their Role in RAG Pipelines
  6. 6How to Select the Right Embedding Model for Your Use Case?
  7. 7Quiz
06Part 1: Collecting, Filtering and Cleaning Data9 lessons
  1. 1Section Overview: Collecting, Filtering, and Cleaning Data for RAG and LLM Pipelines
  2. 2Scraping Data from Websites
  3. 3Scraping via API with Firecrawl
  4. 4Perplexity Web API
  5. 5Web Search API
  6. 6Structuring Your Data: OpenAI Structured JSON Outputs
  7. 7Parsing Data with LlamaParse
  8. 8Choosing Data Sources and Building our AI Tutor Dataset
  9. 9Quiz
07Part 1: Advanced RAG11 lessons
  1. 1Section Overview: Advanced RAG; How we Find and Use the Most Relevant Data
  2. 2Larger Context Window; More Chunks
  3. 3LLM as a judge; Ranking Chunks
  4. 4Search with Metadata Filtering
  5. 5Advanced Search Techniques: From Keywords to Graphs
  6. 6Hybrid search
  7. 7Query Variation and Augmentation
  8. 8Re-Ranking
  9. 9What is Indexing? Indexing Methods for Vector Retrieval
  10. 10Advanced Retrieval
  11. 11Quiz
08Part 1: Fine-Tuning7 lessons
  1. 1Section Overview: Fine-Tuning
  2. 2Optimizing the model: inference and fine-tuning
  3. 3Fine-tuning 101
  4. 4GPT4o-Mini Fine Tuning in our project
  5. 5Is fine-tuning an embedding model worth it? When should you do it?
  6. 6Fine-Tuning Embedding Models with LlamaIndex
  7. 7Quiz
09Part 1: Expanded RAG Toolkit11 lessons
  1. 1Section Overview: Expanded RAG Toolkit
  2. 2Non-Technical Skills for Effective LLM Development
  3. 3Adding Question Validation and Routing
  4. 4An Introduction to Reasoning Language Models
  5. 5Reinforcement Fine-Tuning for OpenAI
  6. 6Enabling Conversational Memory in LLMs
  7. 7Adding Speech with GPT-4o Audio
  8. 8Long Context LLMs vs. RAG
  9. 9Long Context LLMs & Context Caching vs RAG
  10. 10Graph ML and GraphRAG
  11. 11Quiz
10Part 1: APIs, UI and Deployment6 lessons
  1. 1Section Overview: Building and Deploying the Final RAG Chatbot
  2. 2Bringing it all together: Building the final AI Tutor
  3. 3How to make an API?
  4. 4Building and Deploying a Gradio UI on Hugging Face Spaces
  5. 5Quick Tips for Improving LLM Apps Once in Production
  6. 6Quiz
11Part 2: More LLM Capabilities and Useful AI Models6 lessons
  1. 1Part 2 Intro; Introducing more tools, models & frameworks for LLM projects
  2. 2Section Overview: More LLM Capabilities & Other Useful AI Models
  3. 3Image Generation (Diffusion) and Speech models
  4. 4Generating and Editing Images via the OpenAI API
  5. 5LLM Agents: LLM Pipelines and Tool Use for Complex Tasks
  6. 6Quiz
12Part 2: More LLM Frameworks and Tools10 lessons
  1. 1Section Overview: More LLM Frameworks and Tools
  2. 2More LLM framework options; LangChain vs. LlamaIndex vs. OpenAI Assistants
  3. 3LangChain Introduction 101
  4. 4Access More AI Models with Hugging Face's APIs
  5. 5Building Agents with OpenAI Assistants
  6. 6LangChain OpenGPT
  7. 7Extending LLM Capabilities with Anthropic's Model Context Protocol (MCP)
  8. 8Observability or tracing in production; Langsmith, Phoenix and Langfuse
  9. 9More APIs and tools
  10. 10Quiz
13Part 2: LLM Optimization7 lessons
  1. 1Section Overview: LLM Optimizations for Deployment
  2. 2Best tips for Pruning and Distillation (Minitron Paper)
  3. 3Model Distillation and Teacher-Student Models
  4. 4LLM Deployment Optimisation; Quantization, Pruning, Speculative Decoding
  5. 5Deploying a Quantized LLM on CPU on Google Cloud Platform (GCP)
  6. 6Deploying an LLM Using Latitude.sh
  7. 7Quiz
14Part 3: LLM Ecosystem Economics; Choosing Your Niche and Business Strategy10 lessons
  1. 1Part 3 Intro (optional); Deciding which LLM RAG project to build yourself
  2. 2What will drive Enterprise adoption of Generative AI?
  3. 3Who are the Generative AI Winners? How will Adoption Reshape the Economy?
  4. 4Impact of Large Language Models on Different Job Categories
  5. 5175 LLM Project ideas; Choosing your industry, niche, primary function and target users
  6. 6What Makes a Defensible Product Built with Foundation LLMs?
  7. 7AI LLM/RAG deep dive; Perplexity
  8. 8LLM/RAG deep dive; Consensus
  9. 9AI/LLM Deep dive; Harvey AI
  10. 10Quiz
15Part 4: Project + Extra Tools5 lessons
  1. 1Building Your Own advanced LLM + RAG Project to receive certification
  2. 2Project Submission
  3. 3Share your success and showcase your new skills!
  4. 4AI Tutor: Ask any question!
  5. 5Staying Ahead of AI Developments

Updated every two weeks with new lessons as the field evolves.

07The learning experience

How it works

Project-based learning designed for working professionals, fully self-paced.

01

Self-paced, on your schedule

Start the day you enroll, move at your own pace, and revisit any lesson when a project demands it.

02

Learn by building

Hands-on projects from day one. Build a production LLM system from scratch rather than following along with toy examples.

03

Active Discord community

Get unstuck fast. Connect with fellow builders, share progress, and collaborate on projects in an active engineering community.

04

Certificate upon completion

Graduate with certification backed by a real deployed system: portfolio-ready proof you ship.

05

Lifetime access and updates

All current content plus future updates as AI evolves. Your investment stays valuable.

06

Money-back guarantee

30-day full refund, no questions asked. Risk-free enrollment.

08Before you start

Course prerequisites

The projects are hands-on, so a technical foundation matters.

01Intermediate Python knowledge
02Basic knowledge of GitHub
03A device you can set up a coding environment on
04At least 50 hours to gain the full benefit of the course

New to Python? Start with our Beginner Python for AI Engineering course first.

Enrollment

Build your first scalable product

With LLMs, prompting, RAG, tool use, and fine-tuning. $349 one-time, with lifetime access and all future updates.

Unlock access for $349
Secure checkout via Towards AI Academy. 30-day money-back guarantee: if you explore the early lessons and it's not what you need, email us for a full refund.
09Questions

Frequently asked questions

Will I get a certificate upon completion?
Yes, you will receive a Towards AI certificate upon completion.
What projects will I work on?
You'll build your own AI tutor, create RAG pipelines, and ship an advanced LLM MVP. Each project is designed to solve real-world problems, so you graduate with practical, hands-on experience.
What are the course prerequisites?
Intermediate Python, basic GitHub knowledge, and a device you can set up a coding environment on. No prior AI or LLM experience needed.
When was this course created? Is it still up to date?
The course launched in 2025 and is updated every week with new lessons, tools, and techniques as the AI landscape evolves, so the material keeps pace with the field.
How is this course different from other AI courses?
It is fully hands-on, project-based, and focused on the skills top companies are hiring for right now. We guide you through every step, so you understand the concepts and can apply them immediately.
How does this course compare to your "Agent Engineering" course?
Full Stack AI Engineering covers the complete LLM product lifecycle: data collection, RAG, fine-tuning, deployment, and business positioning, with agents as one component. The Agents course goes much deeper into designing, orchestrating, evaluating, and deploying autonomous systems. Compare the Agent Engineering course.
Do you have a referral or affiliate program?
Yes. You can earn cashback, free courses, and up to $70 per course or $180 per bundle in commission by promoting our courses and the "Building LLMs for Production" eBook. See the Affiliate page for tiers and payouts.
Can I get a discount?
Yes, there are three ways to receive a discount. Students get 50% off all courses. If you've previously purchased our book or another course, you're eligible for a discount. We also provide a bundle discount for groups of two or more. Email Louis-François at louis@towardsai.net for a follow-up.
Can I take this course within my company?
Yes. We offer both course bundles and custom training solutions tailored specifically for companies. For company packages or a customized training plan, reach out to Louis at louis@towardsai.net.
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