When companies want an AI that knows their specific internal data they usually face a choice between two paths. Retrieval-Augmented Generation or RAG acts like an open-book exam for the model while fine-tuning is more like intensive studying. Understanding when to use which is critical for anyone building custom business applications.
The Case for Retrieval
RAG is ideal when your information changes frequently like price lists or inventory levels. Instead of retraining the whole model you simply provide a searchable database that the AI can query in real time. This keeps the model current without the massive computational expense of a full training run.
The Precision of Fine Tuning
Fine-tuning is better suited for specialized domains where the model needs to learn a specific tone or complex terminology. By exposing the model to a curated dataset you can change its fundamental behavior and style. For many high-end applications a hybrid approach that uses both methods yields the most reliable results.