LLM Engineering: Prompting, RAG, Fine-Tuning, and RLHF

About this course

Move beyond calling models to engineering them—master prompting, retrieval-augmented generation, parameter-efficient fine-tuning, and preference alignment to build the core engineering foundation behind real-world LLM systems. Agentic AI Mastery: Your 3-Stage Complete Learning Path:

• Course 1: Large Language Models: From Foundations to Transformers
• Course 2: LLM Engineering: Prompting, RAG, Fine-Tuning, and RLHF

Course 3: Agentic AI Engineering: Multimodal, MLOps & AgentsNew to the series? Start with Course 1 to build your core baseline in deep learning, NLP, embedding spaces, vector search, transformer architectures, decoding methods, and LLMs.

Already comfortable with these foundations? You are in the right place. This course turns that theoretical foundation into practical engineering skills. Upon completing this course, you will be fully prepared for Course 3: Agentic AI Engineering: Multimodal, MLOps & Agents.

Why This Course:

Most AI courses stop at "here's how to call an API", leaving you stuck when outputs are unreliable, knowledge is outdated, or a general-purpose model doesn't fit your domain. This course takes a different path.

We bridge the gap between knowing how models work and building systems that work reliably in the real world. You'll master the four techniques that separate AI engineers from API users, prompt engineering, RAG, supervised fine-tuning, and alignment, working ground-up from core principles so your skills stay relevant as tools and frameworks evolve.

Course Modules

Module 1: Large Language Models (Review from Course 1)Refresh and solidify the LLM foundations that everything else builds on. This module revisits how modern LLMs generate text—scaling laws, pre-training, SFT, and preference alignment—and takes you han

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.