RAG vs Fine-Tuning vs Long Context — Where Should Your AI's Knowledge Live?

"Should we use RAG, or fine-tune the model?" It's the question every AI team fights about — and in 2026, it's the wrong question. Here's the mental model that actually tells you what to use, and when.

Stop asking "RAG or fine-tuning?" and start asking: where should my AI's knowledge live? There are exactly three homes. RAG keeps it in an external store and looks it up at query time (an open-book exam) — best for facts that change and for citations. Fine-tuning bakes it into the model's weights — but it's for behavior (style, tone, format), not for facts. Long context just pastes everything into the prompt — perfect for small, one-off jobs, but ~20× pricier at scale, and bigger isn't smarter (the middle gets ignored). We build a dead-simple rubric (knowledge problem → RAG, behavior problem → fine-tune), show why the 2026 default is a hybrid of both — voice from the weights, facts from the store — and name the failure modes (retrieval bugs, catastrophic forgetting, and the classic mistake of fine-tuning in facts).

If you build with LLMs, this is the architecture decision you'll make over and over.
Chapters:
0:00 Intro
0:51 The problem
1:36 RAG
3:15 Fine-tuning
4:46 Long context
5:56 The decision
7:15 Hybrid
8:28 Failure modes
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Watch next:
• Fine-Tuning & LoRA Explained: Behavior, Not Facts → https://youtu.be/l44ZX3YRaXk
• Prompt Engineering: 5 Techniques That Actually Work → https://youtu.be/9zNm0qoUT8c

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Видео RAG vs Fine-Tuning vs Long Context — Where Should Your AI's Knowledge Live? канала datarekha