About the Instructor
Jeremy Howard
Fast.ai co-founder and deep learning practitioner. Former Kaggle President. This RAG & fine-tuning course is delivered via his YouTube channel and covers the production LLM patterns he uses in real projects.
First Principles
RAG (Retrieval-Augmented Generation) solves the biggest LLM problem: hallucination. Instead of relying only on what the model memorized, you give it relevant documents to cite from. Fine-tuning teaches the model your specific language/domain.
AI Ecosystem Fit
Layer 4 → Layer 5 bridge. RAG is how you make LLMs production-ready: accurate, context-aware, verifiable. Fine-tuning specializes general models for specific domains (legal, medical, code).
Prerequisites
What Paid Projects This Unlocks
Build domain-specific agents: document Q&A agents, customer support bots, legal/medical document analysis systems. Deploy context-aware LLM agents.
3 Client Projects You Can Get Paid For
Real businesses have this problem right now. Build it, pitch it, charge for it.
Stack This With → (Unlocks Higher-Paying Projects)
Deploy with LangChain or LlamaIndex
Implement ReAct patterns for multi-step reasoning
Monitor and iterate on domain-specific agents
Why This Matters
RAG is the most cost-effective way to make LLMs accurate without retraining. This is the standard for domain-specific agents in 2026.