Name: Build Your First Product with LLMs, Prompting, RAG
Price: 9 USD
Availability: InStock

Unlock your potential with our comprehensive course that equips you with the skills to build an advanced product using large language models (LLMs). From ideation to deployment, this course covers it all.

Course Highlights

Hands-On Project

This course provides a real "turnkey" project experience. You'll progress through each stage of development by working on your own LLM product. This includes data gathering, building a Retrieval-Augmented Generation (RAG) pipeline, designing the user interface, and deploying the service with OpenAI, LlamaIndex, and Gradio.

Portfolio and MVP Creation

Your final project will serve not just as a learning experience but as a significant portfolio piece or even a minimal viable product (MVP) for a startup or company. It’s a step towards creating tangible value.

Career Advancement

With the rise in demand for LLM Developers, this course positions you to gain a competitive edge in the job market. It prepares you to confidently tackle technical interviews and excel in your career.

Understanding the LLM Ecosystem

Beyond technical techniques like prompt engineering and fine-tuning, you'll delve into the economics of GenAI, niche selection, and monetization strategies, ensuring a deep understanding of the ecosystem.

Scaling LLM Solutions

Learn to adapt LLM solutions for corporate environments, ensuring reliability, accuracy, and security as you scale your user base.

Target Audience

Developers, ML engineers, and data scientists eager to transition into an LLM Developer role
Computer science and AI students poised to make significant career advances
Senior engineers aiming to implement GenAI solutions within their organizations or to launch startups

Prerequisites

Intermediate to advanced proficiency in Python
Basic understanding of Git and GitHub
Commitment to dedicate at least 50 hours for practice and learning

Over more than 50 hours of immersive live sessions and hands-on practice, you'll transform into a professional ready to create, deploy, and scale sophisticated GenAI products. Join us to become one of the leading LLM developers in the field!

About the Authors

Louis-François Bouchard

My journey into the world of AI began in 2019 during my studies in "systems engineering" when I won a competition in emoji classification and realized that I wanted to apply research to real-world tasks. In 2020, I enrolled in a master's program in artificial intelligence, led the AI division at a startup, and launched a YouTube channel dedicated to explaining key AI concepts. These experiences revealed to me a substantial gap between academic research and industry requirements, and in 2022, I became a co-founder of Towards AI to bridge that gap. In 2024, I paused my work on a PhD in medical AI to focus on creating practical solutions. Experience showed that successful AI products require more than just research—they need well-structured technologies and processes. Together with the expert team at TAI, we identified an optimal technology stack for adapting large language models to specific tasks, achieving the necessary accuracy and reliability metrics for a scalable product. Through Towards AI Academy and key projects like the course "From Beginner to Advanced LLM Developer" and an upcoming book, I strive to share these tools and help you create truly effective AI solutions.

Towards AI

Towards AI Academy is an expert online school founded in 2019 with the goal of making application "building" with AI accessible to everyone. Our...

Watch Online 26 lessons

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#1: From Beginner to Advanced LLM Developer | The Towards AI Academy

All Course Lessons (26)

#	Lesson Title	Duration
1	From Beginner to Advanced LLM Developer \| The Towards AI Academy Demo	05:21
2	Why Learn How to Use and Customize LLMs	09:20
3	Part 1: Section Overview: Building Our RAG AI Tutor; Introduction to Using LLMs	02:21
4	To use a LLM or to not use it?	09:16
5	What is Prompting? Talking with AI Models...	04:55
6	What is Prompt Injection? Can you Hack a Prompt?	05:51
7	Part 1: Section Overview: Building Our RAG AI Tutor; Using Basic RAG for Our Project	01:29
8	What is RAG?	09:41
9	Part 1: Section Overview: Building Our RAG AI Tutor; Developing a RAG AI Tutor With LLamaIndex	01:15
10	How vector DBs work and when to use one	11:09
11	RAG Evaluations	10:43
12	Part 1: Section Overview: Building Our RAG AI Tutor; Using Other LLMs and Embedding Models	01:23
13	Part 1: Section Overview: Building our RAG AI Tutor; Advanced RAG; How we Find and Use the Most Relevant Data!	01:34
14	Part 1: Section Overview: Building our RAG AI Tutor; Advanced RAG; How we Find and Use the Most Relevant Data!	01:35
15	Advanced Search Techniques: From Keywords to Graphs	10:31
16	What is Indexing? Indexing Methods for Vector Retrieval	08:36
17	Part 1: Section Overview: Building Our RAG AI Tutor; Fine-Tuning	01:05
18	Optimizing the model - inference and fine-tuning	12:46
19	Is fine-tuning an embedding model worth? When should you do it? Why? What is it?	08:46
20	Part 1: Section Overview: Building Our RAG AI Tutor; Expanded RAG Toolkit	01:39
21	Long Context LLMs vs. RAG	06:47
22	Part 1: Section Overview: Building and Deploying the Final RAG Chatbot	01:20
23	Part 2: Section Overview: More LLM Capabilities & Other Useful AI Models	01:15
24	Part 2 Section Overview More LLM Frameworks and Tools	01:45
25	Part 2: Section Overview: LLM Optimizations for Deployment	01:28
26	Best tips for Pruning and Distillation (Minitron Paper)	13:29

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