One of the most important decisions when building LLM applications is choosing between fine-tuning and RAG. Both techniques improve model performance, but they do so in fundamentally different ways.
What is each technique?
Fine-tuning: Involves additionally training a pre-trained model with domain-specific data. The model learns new patterns and knowledge that become incorporated into its weights.
RAG (Retrieval-Augmented Generation): Adds a document retrieval system that provides relevant context to the model at inference time, without modifying its weights.
When to use Fine-tuning
Fine-tuning is the right choice when you need the model to learn a specific output style or format, when you have a large volume of high-quality labeled data, or when you want to improve performance on very specific tasks like classification or extraction.
It is also preferable when latency is critical, as it does not require database search during inference.
When to use RAG
RAG is superior when your knowledge changes frequently (daily updated documents), when you need to cite verifiable sources, or when working with information the model cannot learn for privacy reasons.
RAG is also the best option if you don't have enough data for fine-tuning or if you need quick implementation without training cycles.
Combining both techniques
Most production systems use both. Fine-tuning trains the model to follow instructions and format, while RAG provides specific knowledge in real-time.
The choice between fine-tuning and RAG is not binary. At Vynta we analyze your specific case to recommend the optimal strategy, whether it is one technique, the other, or a combination. Contact us for a free consultation.