What is Fine-Tuning vs RAG?
Two different approaches to customizing AI — permanent training vs. runtime knowledge injection.
Definition
Fine-tuning permanently changes model weights by training on custom data, making the model inherently better at specific tasks. RAG (Retrieval-Augmented Generation) dynamically retrieves relevant documents at runtime and includes them in the prompt context. Fine-tuning is better for style/format changes; RAG is better for adding up-to-date knowledge without retraining.
💡 Example
A law firm wanting Claude to write in their specific legal style would fine-tune a model. The same firm wanting Claude to reference their case database would use RAG — retrieving relevant cases at query time and providing them as context.
Related concepts
A type of AI trained on massive text datasets to understand and generate human language.
A technique that lets AI access external knowledge bases to provide more accurate answers.
Training a pre-trained AI model on specialized data to improve performance on specific tasks.
A numerical representation of text that captures its meaning as a vector.
Explore AI tools
Find tools that use fine-tuning vs rag in practice.
What is Fine-Tuning vs RAG?
Fine-tuning permanently changes model weights by training on custom data, making the model inherently better at specific tasks. RAG (Retrieval-Augmented Generation) dynamically retrieves relevant documents at runtime and includes them in the prompt context. Fine-tuning is better for style/format changes; RAG is better for adding up-to-date knowledge without retraining.
How does Fine-Tuning vs RAG work in practice?
A law firm wanting Claude to write in their specific legal style would fine-tune a model. The same firm wanting Claude to reference their case database would use RAG — retrieving relevant cases at query time and providing them as context.