RAG vs Fine-Tuning: When Each Wins in Production LLMs

#rag #finetuning #llm #gpt4

The $8,000 Question Nobody Asks Upfront

You need your LLM to answer questions about your company's internal docs. RAG costs you $200/month in embedding API calls and vector DB hosting. Fine-tuning a 7B model runs $500 upfront plus $150/month for inference. Both work. Both have advocates who swear by them.

But here's what most tutorials skip: the decision isn't about which technique is "better." It's about matching the failure mode to your business constraints.

I've deployed both in production. RAG failed spectacularly on a legal contract summarization task—it kept citing irrelevant clauses because semantic search couldn't distinguish "termination for cause" from "termination without cause." Fine-tuning failed on a customer support bot because retraining every time the product docs updated was a 3-day nightmare.

This post walks through the actual decision framework I use in 2026, grounded in what breaks and when.