RAG vs Fine-Tuning: Which Approach Should You Use?

RAG vs fine-tuning comparison showing external knowledge retrieval versus model behavior adaptation

RAG is usually the better choice when an AI system needs current, private, or externally sourced information; fine-tuning is better suited to stable specialized behavior, and both can be combined when an application needs current knowledge plus consistent task-specific behavior.

Complete Guide to Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation connecting an AI language model to external knowledge sources

Retrieval-Augmented Generation (RAG) connects language models to external knowledge so they can retrieve relevant information before generating an answer. This guide explains the architecture, workflow, retrieval process, use cases, limitations, evaluation and production considerations behind reliable RAG systems.