Fine-tuning vs RAG on Azure: Which Should You Use?

Fine-tuning a model to teach it your company's documents? That's usually the wrong move — and it'll cost you a hosting fee and a retrain treadmill. This episode separates the two techniques by the problem they actually solve: RAG changes what the model knows by retrieving your data at query time (chunk, embed, store in Azure AI Search, then ground the prompt), while fine-tuning changes how it acts by adjusting weights on JSONL prompt/completion pairs. The concrete trade-off: RAG makes updates cheap (just re-index) but adds input tokens per call, whereas fine-tuning shrinks prompts but charges you hourly for the custom endpoint whether you use it or not. The gotcha most teams hit — fine-tuning won't reliably fix a knowledge gap, and the two aren't mutually exclusive; production systems often layer fine-tuning for tone and format on top of RAG for grounding. For engineers and architects deciding how to ship a generative AI feature on Azure OpenAI without burning weeks on the wrong approach. ⏱️ Chapters: 0:00 Intro 0:04 The Wrong Choice Costs You 0:37 Two Different Problems 1:13 How RAG Works on Azure 1:51 How Fine-tuning Works 2:30 Cost and Ops Trade-offs 3:13 Where Each One Shines 3:52 A Simple Decision Rule 4:29 Recap and Next Step Subscribe for practical Azure architecture breakdowns every week. Check the current Azure docs — cloud services change. #AzureOpenAI #RAG #FineTuning #AzureAISearch #GenerativeAI