Retrieval-Augmented Generation (RAG) vs LLM Fine-Tuning, by Cobus Greyling

RAG is known for improving accuracy via in-context learning and is very affective where context is important. RAG is easier to implement and often serves as a first foray into implementing LLMs due…

RAG vs. fine-tuning: LLM learning techniques comparison - Addepto

RAG vs. fine-tuning: LLM learning techniques comparison - Addepto

RAG vs. Fine-tuning: Here's the Detailed Comparison, by Amit Yadav

Rethinking Embedding-based Retrieval-Augmented Generation (RAG) for Semantic Search and Large Language Models (LLMs), by Aivin Solatorio

Fine Tuning vs. RAG (Retrieval-Augmented Generation)

Tuning the RAG Symphony: A guide to evaluating LLMs, by Sebastian Wehkamp, Feb, 2024

A New Study Compares RAG & Fine-Tuning For Knowledge Base Use-Cases

Language Model Cascading & Probabilistic Programming Language

Harnessing Retrieval Augmented Generation With Langchain, by Amogh Agastya

Progression of Retrieval Augmented Generation (RAG) Systems – Towards AI

Which is better, retrieval augmentation (RAG) or fine-tuning? Both.

Chain-Of-Note (CoN) Retrieval For LLMs