When building applications with large language models, the choice between RAG (Retrieval-Augmented Generation) and fine-tuning depends on the use case. Here’s a quick breakdown:
RAG: Adds external knowledge to the base model without altering it, making it efficient for dynamic data, explainability, and reducing hallucinations. It’s cost-effective and maintains the model’s general capabilities.
Fine-tuning: Modifies the base model by incorporating specific knowledge, suitable for tasks requiring custom capabilities, low latency, or resource-constrained environments.
Key Scenarios:
Dynamic Data: Use RAG to avoid retraining with frequently changing data.
Custom Capabilities: Fine-tuning is ideal for unique requirements like tone customization.
Explainability & Hallucinations: RAG excels in both areas.
Cost: RAG is less expensive since it avoids training.
Latency: Fine-tuning is faster for low-latency needs.
Resource Constraints: Fine-tuning optimizes smaller models for specific tasks. In some cases, a hybrid approach combining both methods may be best.
For detailed information, please watch our YouTube video: RAG vs. Fine-Tuning: Key Criteria for LLM Projects
