• Lastly, results produced by RAG are more trustworthy.
RAG selects deterministic results from the latest data,
while fine-tuned models may exhibit hallucinations and
inaccuracies when dealing with dynamic data, lacking
transparency and credibility.

3 RAG Framework
The research paradigm of RAG is constantly evolving. This
chapter primarily introduces the evolution of the RAG re-
search paradigm. We categorize it into three types: Naive
RAG, Advanced RAG, and Modular RAG. Although the
early RAG was cost-effective and performed better than the
native LLM, it still faced many shortcomings. The emergence

Feature Comparison

RAG

Fine-tuning

Knowledge Updates

Directly updates the retrieval knowledge
base, ensuring information remains current
without the need for frequent retraining, suit-
able for dynamic data environments.

External Knowledge

Proficient in utilizing external resources,
particularly suitable for documents or other
structured/unstructured databases.

Data Processing
Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey