RAG vs Fine-Tuning vs Long Context: Which One Do You Need?

RAG vs fine-tuning vs long context, explained from zero: which one does your AI project actually need? Fine-tuning shapes tone, style and format but is the wrong tool for facts that change; retrieval-augmented generation keeps answers fresh, citable and permission-aware; long context windows are simplest but cost more per question and can lose details in the middle. A four-question decision guide, the common combinations, and a worked bike-shop example. Part 11 of a series that takes you from zero to building RAG yourself. This is part 11 of the RAG series on Zero 2 Concept: ideas, explained from zero. CHAPTERS 0:00 Three Pieces of Advice 0:50 Fine-Tuning: Teaching Habits 1:53 Where Fine-Tuning Breaks 2:54 RAG: Look It Up 4:09 Long Context: Paste It All 5:23 The Decision Guide 6:41 Better Together 7:30 The Bike Shop Decides 8:44 Try It Yourself 9:29 Checkpoint Previous: Part 10 — Query Rewriting for RAG Explained: Fix the Question, Then Search    • Query Rewriting for RAG Explained: Fix the...   Next: Part 12 — Agentic RAG Explained: When the AI Decides What to Search    • Agentic RAG Explained: When the AI Decides...   Start from Part 1:    • AI Hallucinations Explained: Why AI Lies (...   Full series: RAG from Zero: Retrieval-Augmented Generation Explained    • RAG from Zero: Retrieval-Augmented Generat...