RAG vs fine-tuning in plain words: when to retrieve, when to train

A 4-beat faceless video outline for “RAG vs fine-tuning in plain words: when to retrieve, when to train” in the AI tools explained niche, derived from caption timing of 2 reference videos.

Angles

• Lead with IBM-style definitions: retrieval vs changing model weights vs prompt-only
• Decision tree on screen: private docs? → RAG; stable new skill? → fine-tune; quick tone? → prompt
• Cost and freshness tradeoffs without vendor pitches
• Failure modes: stale index vs overfit fine-tune

Beat-by-beat structure

1. Hook (0:00–0:30): the chatbot invents your company policy — three fixes, not one.
2. Definitions (0:30–2:00): prompt engineering, RAG, fine-tuning in plain words.
3. Decision tree (2:00–5:00): data privacy, update frequency, eval cost.
4. Failure gallery + recap. Original narration only.

Reference videos

• RAG vs. Fine Tuning 00Q0G84kq3M
• RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models zYGDpG-pTho

Method

yt-dlp VTT captions (manual/uploader only) → timed beat heuristics → editorial topic synthesis (no ASR). Structure only; write original narration.