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RAG vs Fine-Tuning
定義
RAG vs fine-tuning is the choice between grounding an AI model in external knowledge at query time (retrieval-augmented generation) versus adjusting the model's own weights on your data (fine-tuning) — two different ways to make a general model useful for your specific needs.
How they differ
RAG keeps the model as-is and feeds it relevant information — help articles, docs, past tickets — retrieved at the moment of each query. The model's knowledge stays current because you just update the source content, and it's easy to trace an answer back to its source. Fine-tuning instead retrains the model on your examples so the behavior is baked into its weights; changing what it knows means retraining.
In practice they solve different problems: RAG is best for injecting up-to-date facts and knowledge; fine-tuning is best for shaping style, format, or specialized behavior the base model doesn't do well.
- RAG: dynamic knowledge, easy to update, source-traceable
- Fine-tuning: adjusts model behavior, style, and format
- The two are complementary, not mutually exclusive
Why it matters for support
For customer support, RAG is usually the workhorse: your policies, pricing, and product details change constantly, and you want answers grounded in the current help center — not frozen into a model at training time. Fine-tuning may layer on top to enforce tone or a specific reply format, but it's not how you keep the facts fresh.
よくある質問
Should I use RAG or fine-tuning for a support bot?
Usually RAG for the knowledge, since your content changes often and RAG keeps answers current and traceable. Fine-tuning is optional on top, mainly to shape tone or output format — not to store facts.
Can you use RAG and fine-tuning together?
Yes. They address different things: fine-tuning shapes how the model behaves, and RAG supplies up-to-date knowledge. Many production systems combine both.
関連用語
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique where an AI model retrieves relevant information from your own knowledge sources at query time and uses it to ground its answer, instead of relying only on what it memorized during training..
Fine-Tuning
Fine-tuning is the process of further training a pre-trained language model on a smaller, task-specific dataset so it adapts to a particular domain, style, or behavior — updating the model's weights rather than just its instructions..
Grounding
Grounding is the practice of tying an AI model's answers to verified source material — your documentation, live data, or knowledge base — so responses reflect real facts rather than the model's own guesses..
Large Language Model (LLM)
A large language model (LLM) is a neural network trained on vast amounts of text to predict and generate language, enabling it to understand questions, summarize, classify, and write human-like responses..
Knowledge Base
A knowledge base is a structured, searchable library of articles — how-tos, FAQs, troubleshooting guides, and policies — that lets customers or agents find answers without contacting support directly..
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Macha は既に運用中のヘルプデスク (Zendesk、Freshdesk、Front、Intercom、Gorgias) の上に載る AI エージェント層です。
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