RAG vs Fine-Tuning: Choosing the Right AI Strategy with Azure OpenAI
RAG vs Fine-Tuning: Real Engineering Decisions with Azure OpenAI RAG vs Fine-Tuning Real Engineering Decisions with Azure OpenAI (.NET Perspective) 1. The Problem Most Teams Get Wrong Most teams entering AI make a critical mistake: they assume they need fine-tuning to solve everything. In reality, they are trying to solve a data problem with a model training solution . Hard truth: If your knowledge changes frequently, fine-tuning will become your most expensive mistake. The real question is not “Which is better?” but: Are you solving knowledge retrieval or behavior control? --- 2. RAG — The Default Strategy You Should Start With Retrieval-Augmented Generation (RAG) keeps your model static and injects fresh data at runtime. Production Flow 1. Convert documents into embeddings 2. Store in vector database 3. Retrieve top matches 4. Inject into prompt 5. Generate response Engineering Tip: The bottleneck in RAG is not the LLM, it is retriev...