Build Your First Product with LLMs, Prompting, RAG

Name: Build Your First Product with LLMs, Prompting, RAG
Price: 12.75 USD
Availability: InStock
Rating: 5 (1 reviews)

2h 25m 20s

English

Paid

June 16, 2025

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

Louis-François Bouchard is a French-Canadian AI engineer and educator behind the What's AI newsletter and YouTube channel — one of the more accessible explainer sources on modern AI research. He is also the lead instructor for several courses on the Towards AI platform, where he teaches the production-engineering side of LLM applications.

His CourseFlix listing carries six Louis-François Bouchard courses spanning the applied AI track: Building LLMs for Production, 10-Hour LLM Fundamentals, Build Your First Product with LLMs / Prompting / RAG, Master AI for Work, Beginner Python Primer for AI Engineering, and the Agentic AI Engineering Course.

Material is paid and aimed at engineers picking up applied LLM work as a serious skill. For broader content, see CourseFlix's LLMs & Fundamentals, RAG, and AI Agents category pages.

Towards AI

Towards AI is one of the larger AI-focused publishers on the open web — originally a Medium publication and now a multi-author content platform plus a paid course catalog focused on production LLM engineering. The brand has tracked the post-ChatGPT generative-AI wave from inside the field rather than from a generic SaaS-marketing perspective.

The CourseFlix listing reflects their applied focus: Building LLMs for Production, 10-Hour LLM Fundamentals, Build Your First Product with LLMs, Prompting, RAG, the Agentic AI Engineering Course, Beginner Python Primer for AI Engineering, and Master AI for Work. Material is paid and aimed at engineers who already know Python and want to ship production AI features rather than read a survey of the field.

Watch Online 26 lessons

0:00

#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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Frequently asked questions

What are the prerequisites for enrolling in this course?

The course is designed for developers, machine learning engineers, and data scientists who are eager to explore the capabilities of large language models (LLMs). While prior experience in programming and familiarity with AI concepts would be beneficial, the course starts from the basics and progresses to advanced topics, ensuring a comprehensive learning journey.

What practical project will I build during the course?

Throughout the course, you will work on developing a Retrieval-Augmented Generation (RAG) pipeline project. This includes tasks such as data gathering, designing a user interface, and deploying a service using tools like OpenAI, LlamaIndex, and Gradio. The final product can serve as a portfolio piece or even as a minimal viable product (MVP) for startups or companies.

How does this course compare in depth and scope to other courses?

This course provides an extensive look into the LLM ecosystem, ranging from basic prompting techniques to advanced scaling solutions for corporate environments. It not only covers technical aspects but also delves into the economics of generative AI, niche selection, and monetization strategies, offering a holistic view that might not be available in more narrowly focused courses.

Which specific tools and platforms are covered in the course?

The course extensively covers tools such as OpenAI, LlamaIndex, and Gradio for building and deploying LLM-based applications. You'll also learn about the use of vector databases and advanced search techniques. These tools are integral to the hands-on project component, where you will build a RAG pipeline.

What topics are not covered in this course?

While the course is comprehensive in covering LLMs and RAG pipelines, it does not delve into the foundational aspects of machine learning or deep learning algorithms. It assumes a level of familiarity with AI concepts and focuses specifically on the application of LLMs rather than the underlying machine learning models.

How much time should I expect to commit to this course?

The course consists of 26 lessons, but the total runtime is not specified. Given the depth and hands-on nature of the course, students should anticipate dedicating several hours per week for both lectures and project work to fully understand the material and complete the project.

What value does the course offer for career advancement?

With the growing demand for LLM developers, the course equips you with the skills needed to gain a competitive edge in the job market. It prepares you for technical interviews and enhances your ability to design scalable LLM solutions, making you a valuable candidate for roles in AI and data-driven industries.