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 Evaluation

Don't Build LLM Apps…Before Knowing About RAG, by John Adeojo

12 Prompt Engineering Techniques, PDF, Cognitive Science

Retrieval augmented generation: Keeping LLMs relevant and current - Stack Overflow

Introduction To Retrieval Augmented Generation - Arize AI

Fine Tuning vs. RAG (Retrieval-Augmented Generation)

Tips on What To Do With Your Language Model or API, by Louis-François Bouchard

Retrieval-Augmented Generation: How to Use Your Data to Guide LLMs

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

RAPTOR: Supercharge your RAG with Deeper Context Understanding, by AI TutorMaster, Mar, 2024

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

T-RAG = RAG + Fine-Tuning + Entity Detection

RAGs from scratch — Why & What?!!, by Arion Das, Feb, 2024

Visualize your RAG Data — Evaluate your Retrieval-Augmented Generation System with Ragas, by Markus Stoll, Mar, 2024

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