You need an LLM app to answer questions about your company's internal docs, which are updated weekly. Should you RAG or fine-tune, and what factor most cleanly tips the decision?

The right answer is **RAG**, and the factor that tips it is how often the knowledge changes. Think about what each approach does with facts. Fine-tuning bakes knowledge into the model's weights through a training run. RAG keeps the knowledge in a separate searchable index and looks it up at query time. The model itself is equally smart either way; only the location of the facts differs. Now apply the weekly update detail. With fine-tuning, every time the docs change you have to run another training cycle. That is slow, costs money, and can make the model forget things it learned before. With RAG, you just re-embed the new or changed documents and add them to the index. That takes minutes and touches nothing else. So for docs that update weekly, RAG wins on operational cost without giving up answer quality. The trap answer claims fine-tuned knowledge is deeper, but for looking up facts that is simply not true. The model reads the retrieved text and reasons over it the same way either way.

Choose **RAG**. The cleanly tipping factor is knowledge-change frequency, and weekly updating docs put this firmly in RAG territory. The core distinction: RAG decouples the knowledge layer from the model. Your docs live in a vector index; you retrieve relevant chunks at query time and feed them to the LLM as context. Fine-tuning instead encodes knowledge into the model's parameters during a training run. That decoupling is exactly what the weekly cadence rewards. Updating a RAG index is incremental and cheap: re-embed the changed documents, upsert them, done in minutes. Re-fine-tuning is the opposite: a full training cycle each week, with real GPU cost, latency in hours, and the risk of catastrophic forgetting where new training degrades previously learned behavior. The tempting wrong answer is that fine-tuning gives 'deeper' knowledge. For factual recall that is empirically false; the LLM synthesizes over retrieved context with the same reasoning capacity. Fine-tuning earns its keep for behavior: output format, domain voice, tool-call structure. Hybrid (fine-tune the voice, RAG the facts) is sometimes the production answer, but the question asks for the single most-cleanly tipping factor, and that is update frequency.