llm_rag_vs_fine_tuning_cheatsheet.md

Consideration	RAG	Fine-Tuning	Notes
Data Requirements	Less reliant on domain-specific training data	Requires substantial domain-specific training data	RAG adapts easily to new data; fine-tuning is best for stable knowledge bases.
Model Adaptability	More flexible to new, unseen data	Less flexible, tailored to the training data	RAG is preferred for applications needing up-to-date information.
Update Frequency	Easily incorporates new information without retraining	Requires retraining for new information	RAG is beneficial for dynamic content generation.
Performance	Best for tasks requiring external data	Best for tasks with a stable knowledge base	Fine-tuning excels in domain-specific applications.
Cost	Lower computational cost for updates	Higher computational cost due to retraining	Fine-tuning may lead to cost savings with small models.
Data Dynamics	Excels in dynamic environments, updating from sources	Static snapshot of data, may become outdated	RAG is preferred for evolving information needs[1].
Customization	May not fully customize model's behavior or style	Deep alignment with specific styles or knowledge	Fine-tuning for specialized styles or deep domain alignment[1][4].
Hallucination	Less prone due to grounding in retrieved evidence	May fabricate responses but can reduce hallucinations	RAG minimizes hallucinations[1].
Transparency	Offers transparency in response generation	Operates more like a black box	RAG advantages in transparency and interpretability[1].
Cost Efficiency	Does not allow for use of smaller models	Improves effectiveness of small models	Fine-tuning preferable for cost considerations[1].
Knowledge Injection	Dynamic incorporation of external knowledge	Injects new knowledge, can be unstable	RAG for tasks requiring up-to-date or domain-specific knowledge[2][4].
Use Cases	Ideal for querying databases or documents	Suited for specific outcomes or behavioral adjustments	RAG for external data reliance; fine-tuning for behavioral changes[1][5].
Combination	-	-	Combining RAG and fine-tuning leverages strengths of both[3].