[ TECHNICAL COMPARISON SPEC ]
RAG vs. Fine-Tuning: LLM Architecture Comparison
RAG injects dynamic internal documentation into LLM prompts at query time via vector databases. Fine-tuning updates the internal neural weights of an open-source model.
[ OPTION A ]
Retrieval-Augmented Generation (RAG)
Vector database lookup engine retrieving exact document chunks into prompt contexts.
Advantages:
• Real-time document updates without model retraining
• 100% verifiable source citations
• Prevents model hallucinations
[ OPTION B ]
Model Fine-Tuning
Training model weights on specific dataset formatting to internalize tone or jargon.
Advantages:
• Teaches customized output syntax and style
• Reduces prompt token overhead
[ ARCHITECTURAL VERDICT ]
Use RAG for enterprise document search, policy lookups, and dynamic databases. Use Fine-Tuning when you need custom model syntax or domain-specific tone formatting.
Frequently Asked Questions
Can we combine RAG and Fine-Tuning?
Yes. Many enterprise systems use a fine-tuned model for domain syntax while feeding it dynamic context via RAG.