English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 26 Lessons (2h 25m) | 1.10 GB
From Beginner to Advanced LLM Developer. Build Your First Scalable Product with LLMs, Prompting, RAG, Fine-Tuning, and Agents!
Full Stack LLM Developer Conversion in a Single Course!
Master the new skills top companies need and build your own working advanced LLM product
Build Your Advanced LLM Project
From helping choose your own project through to deployment, you’ll gain hands-on experience building a real-world LLM product. This could become a portfolio project that gives you a competitive edge in the job market, a Minimum Viable Product for a startup idea, or the start of a new product at your company.
Land that LLM Developer Role
The LLM Developer role is only 2-3 years old and there are no true experts yet. Build this skillset early and help lead the LLM revolution! Build your portfolio and walk into your next interview with the confidence that you can create, deploy, and manage Generative AI solutions at scale.
Become One of the Top LLM Developer Needed for Widescale GenAI Adoption at Enterprise
LLM Pipelines are already creating new unicorns and $200m+ enterprise cost savings. But reliable LLM products that really boost productivity need lots of customisation and development on top of foundation LLMs. Products must be developed for specific industry niches, companies and use cases.
Table of Contents
1 From Beginner to Advanced LLM Developer | The Towards AI Academy
2 Why Learn How to Use and Customize LLMs
3 Part 1: Section Overview: Building Our RAG AI Tutor; Introduction to Using LLMs
4 To use a LLM or to not use it?
5 What is Prompting? Talking with AI Models…
6 What is Prompt Injection? Can you Hack a Prompt?
7 Part 1: Section Overview: Building Our RAG AI Tutor; Using Basic RAG for Our Project
8 What is RAG?
9 Part 1: Section Overview: Building Our RAG AI Tutor; Developing a RAG AI Tutor With LLamaIndex
10 How vector DBs work and when to use one
11 RAG Evaluations
12 Part 1: Section Overview: Building Our RAG AI Tutor; Using Other LLMs and Embedding Models
13 Part 1: Section Overview: Building our RAG AI Tutor; Advanced RAG; How we Find and Use the Most Relevant Data!
14 Part 1: Section Overview: Building our RAG AI Tutor; Advanced RAG; How we Find and Use the Most Relevant Data!
15 Advanced Search Techniques: From Keywords to Graphs
16 What is Indexing? Indexing Methods for Vector Retrieval
17 Part 1: Section Overview: Building Our RAG AI Tutor; Fine-Tuning
18 Optimizing the model – inference and fine-tuning
19 Is fine-tuning an embedding model worth? When should you do it? Why? What is it?
20 Part 1: Section Overview: Building Our RAG AI Tutor; Expanded RAG Toolkit
21 Long Context LLMs vs. RAG
22 Part 1: Section Overview: Building and Deploying the Final RAG Chatbot
23 Part 2: Section Overview: More LLM Capabilities & Other Useful AI Models
24 Part 2 Section Overview More LLM Frameworks and Tools
25 Part 2: Section Overview: LLM Optimizations for Deployment
26 Best tips for Pruning and Distillation (Minitron Paper)
Resolve the captcha to access the links!