Build Your First Product with LLMs, Prompting, RAG
2h 25m 20s
English
Paid
Course description
This practical intensive course will provide you with all the necessary skills to create a fully functional advanced product based on large language models (LLM) - from choosing an idea to deploying it in production.
Read more about the course
What you will get:
- A real "turnkey" project. At every stage (from concept to deployment), you will work on your own LLM product: gathering data, building a Retrieval-Augmented Generation (RAG) pipeline, designing the user interface, and deploying the service using OpenAI, LlamaIndex, and Gradio.
- Portfolio and MVP. The final project will not just be a study assignment but a full-fledged portfolio element or a minimal viable product for your startup or company.
- Career as an LLM Developer. The role of an LLM developer is one of the most in-demand and emerging in the industry. You will gain a competitive advantage in the job market and confidently pass technical interviews.
- Deep understanding of the ecosystem. You will learn not only technical techniques (prompt engineering, fine-tuning, complex RAG channels) but also the economics of GenAI, niche selection, and monetization strategy.
- Scaling skills. Discover how to adapt LLM solutions to corporate requirements: ensuring reliability, accuracy, and security as user numbers grow.
Who it's for:
- Developers, ML engineers, and data scientists looking to quickly master the profession of LLM Developer
- Computer science and AI students preparing for a career leap
- Senior engineers planning to implement GenAI solutions in their company or launch their own startup
What you need to know beforehand:
- Confident in Python (intermediate or higher)
- Basic knowledge of Git and GitHub
- Willingness to dedicate at least 50 hours of practice to the course
In over 50 hours of live sessions and practice, you will become a professional capable of creating, deploying, and scaling complex GenAI products. Join and become one of the leading LLM developers!
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| # | Title | Duration |
|---|---|---|
| 1 | From Beginner to Advanced LLM Developer | The Towards AI Academy | 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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