RAG vs fine-tuning: which one do you need?

After this lesson you can: Choose correctly between retrieval and fine-tuning for a given problem.

First: Build a document Q&A app

Watch

• RAG vs. Fine Tuning - IBM Technology, 9 min. Exact short concept match from a vetted channel, focused directly on choosing RAG versus fine-tuning.
• RAG vs Fine Tuning vs Prompt Engineering - codebasics, 9 min. Concise comparison likely useful for engineers deciding between prompting, retrieval, and model adaptation.
• Prompt Engineering Vs RAG Vs Finetuning Explained Easily - Krish Naik, 18 min. Slightly broader but still beginner-friendly and directly contrasts RAG, fine-tuning, and prompt engineering.

Notes

RAG and fine-tuning solve different problems. RAG gives a model access to facts at inference time by retrieving relevant documents and placing them into the prompt. Fine-tuning changes the model’s behavior by training it on examples, usually to make it follow a style, format, workflow, or domain-specific pattern more reliably.

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