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Free Download LLM Engineering Prompting, RAG, Fine-Tuning, and RLHF
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 32m | Size: 6.3 GB
Build Production LLM Systems: prompt engineering, RAG pipelines, PEFT fine-tuning (LoRA), and RLHF (DPO/PPO) alignment.
What you'll learn
Design advanced prompts-zero-shot, few-shot, and chain-of-thought reasoning-that reliably produce high-quality, structured outputs at scale.
Architect and build complete RAG systems-covering ingestion, retrieval, and synthesis-then evaluate them rigorously for accuracy and grounding.
Fine-tune base models into specialized assistants using parameter-efficient methods like LoRA, QLoRA, rsLoRA, and LoRA-FA.
Align LLM behavior to human preferences using the full RLHF workflow and algorithms including PPO, DPO, and GRPO.
Combine prompting, retrieval, fine-tuning, and alignment into an end-to-end, production-ready LLM system.
Requirements
Understanding of Transformers & LLMs: Familiarity with neural networks, attention, and how LLMs are trained and generate text (Course 1 or equivalent knowledge).
Intermediate Python: Comfort reading and writing functions, loops, and basic data structures (code is explained step-by-step, but you should be comfortable reading Python).
Basic ML Intuition: Comfort with vectors, embeddings, and core training concepts like loss and gradients (all key ideas are refreshed intuitively throughout the course).
Description
Move beyond calling models to engineering them-master prompting, retrieval-augmented generation, parameter-efficient fine-tuning, and preference alignment to build production-grade 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 & Agents
New 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 in production. 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 hands-on with decoding strategies and hallucination inspection, ensuring you're ready to engineer these models rather than just use them.
Module 2: Prompt Engineering
Master the craft of communicating with LLMs. Move from prompting fundamentals through proven engineering techniques to advanced strategies, learning to design prompts that consistently produce high-quality, reliable outputs across any application.
Module 3: Retrieval-Augmented Generation (RAG)
Build AI systems that combine LLMs with external knowledge. Walk through every stage of the RAG pipeline-ingestion, retrieval, and synthesis-then learn to rigorously evaluate your system, enabling responses that are accurate, up-to-date, and verifiable.
Module 4: Supervised Fine-Tuning (SFT)
Transform general-purpose models into specialized experts. Explore the limits of in-context learning, the fundamentals of fine-tuning, and efficient methods like LoRA and QLoRA (PEFT)-mastering datasets and training dynamics to adapt models to your domain without massive compute.
Module 5: Reinforcement Learning from Human Feedback (RLHF)
Shape model behavior to match human preferences. Understand why alignment matters, the reinforcement learning fundamentals behind it, and the full RLHF workflow-including PPO, DPO, and GRPO alignment algorithms.
This Course Features
-33 Bite-Sized FHD Video Lessons: Clear, structured lectures breaking down prompting, RAG, fine-tuning, and alignment across 5 comprehensive modules.
-5 Hands-on Coding Labs (Industry-Inspired Mini-Projects): Implement decoding inspection, prompt engineering pipelines, RAG systems, parameter-efficient fine-tuning, and preference alignment using industry-standard tools.
-5 Deep Dive Audio Podcasts: Reinforce every module's concepts on the go with custom podcast discussions.
-Industry-Standard Tools & Ecosystem: Hands-on experience with production-grade frameworks and libraries used to build, retrieve, fine-tune, and align real LLM systems.
-Intuitive Visual Diagrams: Complex mechanics, like RAG pipelines, LoRA/QLoRA/LoRA-FA/rsLoRA, the RLHF workflow, and PPO vs. DPO vs. GRPO, explained through clean visual architecture diagrams.
-5 MCQ Quizzes: Evaluate your knowledge and understanding at the end of every module.
-3-Part Series Progression: The essential middle stage connecting the foundations of Course 1 to the advanced agentic engineering in Course 3.
Who this course is for
Software Engineers & Developers ready to move from calling LLM APIs to architecting reliable, production-grade LLM systems.
Data Scientists & ML Practitioners who understand model basics and want practical skills in RAG, fine-tuning, and alignment.
Computer Science Students & Tech Professionals seeking the engineering patterns used by leading AI teams to customize and deploy language models.
Prerequisites
Understanding of Transformers & LLMs: Familiarity with neural networks, attention, and how LLMs are trained and generate text (Course 1 or equivalent knowledge).
Intermediate Python: Comfort reading and writing functions, loops, and basic data structures (code is explained step-by-step).
Basic ML Intuition: Comfort with vectors, embeddings, and core training concepts (all key ideas are refreshed intuitively throughout the course).
New to transformers and LLMs? We recommend starting with Course 1: Large Language Models: From Foundations to Transformers.
What You'll Master
Design advanced prompts, zero-shot, few-shot, and chain-of-thought reasoning, that reliably produce high-quality outputs.
Architect and build complete RAG systems, from ingestion and retrieval to synthesis, then evaluate them rigorously.
Fine-tune models efficiently for specific domains using parameter-efficient methods like LoRA, rsLoRA, LoRA-FA and QLoRA.
Align model behavior to human preferences using the RLHF workflow, PPO, DPO, and GRPO.
Combine prompting, retrieval, fine-tuning, and alignment into optimized, production-ready LLM solutions.
Join us for the practical core of the Agentic AI Mastery series and build the engineering skills that turn general-purpose models into powerful, specialized systems.
Module 1: Large Language Models (Review - Module 5 from Course 1)
Video Lessons
- Lesson 1.1: Language Models - From N-grams to Neural
- Lesson 1.2: Scaling Laws and the Discovery of LLMs
- Lesson 1.3: How LLMs Learn: Pre-training, SFT, and Preference Alignment
- Lesson 1.4: Decoding Strategies for Text Generation
- Lesson 1.5: Current Limitations and Challenges
- Lesson 1.6: Preview of LLM Engineering Techniques
Resources
Module 1 summary notes
- Quiz 1
- Lab 1 - Text Generation: Decoding, Creativity & Hallucination
- Lab 1 (Deep Dive Podcast)
Module 2: Prompt Engineering
Video Lessons
- Lesson 2.1: Fundamentals of Prompting
- Lesson 2.2: Prompt Engineering Techniques
- Lesson 2.3: Advanced Techniques
- Lesson 2.4: Lab Overview
Resources
- Module 2 summary notes
- Quiz 2
- Lab 2 - Prompt Engineering: Building a Complaint Router (Instructing LLMs at Scale)
- Lab 2 (Deep Dive Podcast)
Module 3: Retrieval-Augmented Generation (RAG)
Video Lessons
- Lesson 3.1: Significance of RAG
- Lesson 3.2: RAG Pipeline
- Lesson 3.3: Stage 1 - Ingestion
- Lesson 3.4: Stage 2 - Retrieval
- Lesson 3.5: Stage 3 - Synthesis
- Lesson 3.6: RAG Evaluation
- Lesson 3.7: Lab Overview
Resources
- Module 3 summary notes
- Quiz 3
- Lab 3 - RAG: Building a Medical Q&A Engine (Grounding the Answer)
- Lab 3 (Deep Dive Podcast)
Module 4: Supervised Fine-Tuning (SFT)
Video Lessons
- Lesson 4.1: The Limits of In-Context Learning
- Lesson 4.2: Fundamentals of Fine-Tuning
- Lesson 4.3: Efficient Fine-Tuning with PEFT (LoRA, rsLoRA, LoRA-FA & QLoRA)
- Lesson 4.4: SFT Datasets and Training Dynamics
- Lesson 4.5: Lab Overview
Resources
- Module 4 summary notes
- Quiz 4
- Lab 4 - SFT: Turning a Base Model into an Assistant (Teaching Behavior)
- Lab 4 (Deep Dive Podcast)
Module 5: Reinforcement Learning from Human Feedback (RLHF)
Video Lessons
- Lesson 5.1: Why RLHF?
- Lesson 5.2: Fundamentals of Reinforcement Learning
- Lesson 5.3: RLHF Workflow
- Lesson 5.4: Alignment Algorithms (PPO, DPO, GRPO)
- Lesson 5.5: Lab Overview
Resources
- Module 5 summary notes
- Quiz 5
- Lab 5 - RLHF: Aligning an LLM for Safety (Aligning the Values)
- Lab 5 (Deep Dive Podcast)
Who this course is for
Software Engineers & Developers wanting to transition from standard application development to AI/LLM engineering.
Data Scientists & ML Beginners looking to move past traditional scikit-learn pipelines into modern Transformer architectures.
Computer Science Students & Tech Professionals who want a deep, principled understanding of how LLMs work under the hood rather than just calling third-party APIs.
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