When Should You Use RAG vs Fine-Tuning in Microsoft Foundry?
If you've been working with Microsoft Foundry, you've likely come across this question: Should I use RAG or Fine-Tuning? The answer becomes much simpler when you focus on the core goal of your solution. Here's a straightforward way to think about it. What is RAG (Retrieval-Augmented Generation)? RAG allows your model to retrieve relevant information from your data sources before generating a response. Instead of relying only on what the model already knows, it: Searches your documents or knowledge base Retrieves relevant content Uses that context to generate grounded, cited answers Use RAG when: ✅ Your data changes frequently ✅ You need answers based on real documents ✅ You have a large, evolving document library ✅ You are building "chat with your data" experiences What is Fine-Tuning? Fine-tuning customizes how the model behaves by training it on task-specific examples. It helps the model: Produce consistent and structured outputs Follow a specific tone, format, or brand voice Align with business rules, compliance policies, and workflows Use Fine-Tuning when: ✅ You need consistent and predictable responses ✅ You want a specific tone, format, or behavior ✅ Your task is stable and well-defined ✅ You are operating at massive scale Visual Overview Below is a quick visual summary to help compare both approaches: A Simple Way to Decide Ask yourself: Is my problem about accessing the right data, or controlling how the model behaves? If it's about data → use RAG If it's about behavior → use Fine-Tuning Quick Comparison What You Need RAG Fine-Tuning Data changes often ✅ Yes ❌ Not ideal Change model behavior/style ❌ No ✅ Yes Fast to get started ✅ Faster ❌ Needs training High-volume, stable queries ⚠️ Token costs grow ✅ Predictable cost Brand voice / compliance ⚠️ Limited ✅ Built into model Large, evolving document library ✅ Perfect fit ❌ Hard to maintain Can You Use Both? In many real-world scenarios, the best teams do exactly that: RAG brings in the right, up-to-date information Fine-Tuning ensures consistent behavior and output quality Think of RAG as giving your model the right books to read, and Fine-Tuning as teaching it how to think and respond. Together, they cover both sides of the equation. I'd love to hear from others in the community: Are you using RAG, Fine-Tuning, or both in your Foundry projects? What use cases are you solving? What challenges or trade-offs have you experienced along the way? Looking forward to your insights. Let's learn from each other! 🚀Cancer Survival with Radiology-Pathology Analysis and Healthcare AI Models in Azure AI Foundry
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