Fine-TuningRAGLLMAI
Teams often assume they need to fine-tune a model to make it 'know their business'. Usually they need RAG instead. Here's the honest decision framework.
RAG: Inject Knowledge at Runtime
- Best for knowledge — facts, docs, policies that change over time.
- Update by changing documents, not retraining. Always current.
- Provides citations and is far cheaper and faster to ship.
Fine-Tuning: Teach Behavior and Style
- Best for behavior — a consistent format, tone, or a narrow specialized task.
- Requires a quality labeled dataset and a training/eval pipeline.
- Knowledge baked in goes stale; updating means retraining.
The Rule of Thumb
“RAG is for what the model should know. Fine-tuning is for how the model should behave.”
Start With RAG and Prompting
Most 'we need fine-tuning' problems are solved by better retrieval and a sharper prompt. Exhaust those first — they're cheaper, faster, and easier to maintain.
